A reasoning method of a financial risk early warning large language model

Abstract

The application relates to a reasoning method of a financial risk early warning large language model. The method comprises the following steps: performing event evidence processing on structured financial data and unstructured text data corresponding to a financial risk early warning subject to obtain event evidence data; performing graph structure fusion on subject identification data, subject relationship data and event type data in the event evidence data to obtain risk evidence graph data; performing large language model mechanism chain analysis on target subgraph data in the risk evidence graph data to obtain causal mechanism chain data; performing assertion compilation on the causal mechanism chain data to obtain assertion test result data; and performing false alarm constraint calibration on the risk evidence graph data and the causal mechanism chain data according to the assertion test result data to obtain financial risk early warning result data. The method can improve the early warning accuracy and advance quantity and reduce the artificial review cost.

Description

Technical Field

[0001] This application relates to the field of intelligent early warning technology, and in particular to a reasoning method for a large language model for financial risk early warning. Background Technology

[0002] Traditional financial risk early warning technologies typically rely on the fusion of multi-source data (macroeconomic indicators, market conditions, financial statements, transaction and public opinion texts, network relationships, etc.). Risk characterization is achieved through feature engineering and indicator system construction (such as leverage, liquidity, maturity mismatch, volatility, correlation, default / migration rate, etc.). Statistical econometrics (Logit / Probit, Cox Survival Analysis, GARCH / VAR, stress testing, VaR / ES, credit scoring, and migration matrices) are then employed to emphasize interpretability and robust estimation. However, traditional technologies generally suffer from insufficient characterization of extreme tail events and structural mutations, weak cross-market / cross-cycle generalization capabilities, and biases due to data quality and scarce labels, resulting in low accuracy in financial risk early warning. Summary of the Invention

[0003] Therefore, it is necessary to provide a reasoning method for a large language model of financial risk early warning that can improve the accuracy and lead time of early warning, in order to address the above-mentioned technical problems.

[0004] Firstly, this application provides a reasoning method for a large language model for financial risk early warning, including: The structured financial data and unstructured text data corresponding to the financial risk warning entities are processed into event evidence data. The subject identification data, subject relationship data, and event type data in the event evidence data are fused using a graph structure to obtain risk evidence graph data; A large language model mechanism chain analysis is performed on the target subgraph data in the risk evidence graph data to obtain causal mechanism chain data; The causal mechanism chain data is compiled with assertions to obtain assertion test result data; Based on the assertion test results, the risk evidence graph data and the causal mechanism chain data are calibrated against false alarm constraints to obtain financial risk early warning results.

[0005] The aforementioned reasoning method of a large language model for financial risk early warning unifies the processing of structured financial data and unstructured text data into event evidence, constructing a risk evidence graph with evidence sources, credibility, and timestamps. This enables traceable fusion of risk information under subject relationships and time constraints. The large language model then generates causal mechanism chains on the target subgraph that can be bound to evidence citations. These chains are further asserted and compiled into a machine-verifiable set of assertions to output assertion verification results. This transforms the risk explanation of "relevance narratives" into a "falsifiable and reproducible" reasoning process, significantly reducing false positives and false negatives caused by the illusion of the large language model. Finally, based on the assertion verification results, the risk evidence graph and causal mechanism chains are calibrated to constrain false positives, ensuring that the early warning trigger threshold is controllable within a given upper limit for the false positive rate. The output includes early warning results containing key evidence, key mechanism segments, and key assertions, improving the robustness, consistency, and regulatory auditability of risk early warnings. This, in turn, increases the accuracy and lead time of early warnings while reducing the cost of manual review. Attached Figure Description

[0006] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0007] Figure 1 This is a flowchart illustrating the reasoning method of a large language model for financial risk early warning in one embodiment; Figure 2 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0009] In one exemplary embodiment, such as Figure 1 As shown, a reasoning method for a large language model for financial risk early warning is provided. Taking the application of this method to a server as an example, the method includes the following steps 102 to 110. Wherein:

[0010] Step 102: Perform event evidence processing on the structured financial data and unstructured text data corresponding to the financial risk warning subject to obtain event evidence data.

[0011] Step 104: Perform graph structure fusion on the subject identification data, subject relationship data and event type data in the event evidence data to obtain risk evidence graph data.

[0012] Step 106 involves performing a large language model mechanism chain analysis on the target subgraph data in the risk evidence graph data to obtain causal mechanism chain data.

[0013] Step 108: Perform assertion compilation on the causal mechanism chain data to obtain assertion test result data.

[0014] Step 110: Based on the assertion test results, perform false alarm constraint calibration on the risk evidence graph data and the causal mechanism chain data to obtain financial risk early warning results data.

[0015] Among them, the financial risk early warning subject is the object entity that needs to be monitored and from which risk early warning conclusions are output, including but not limited to companies, institutions, accounts, products, counterparties or combinations thereof.

[0016] Structured financial data refers to financial data with fixed fields and clear data types that can be directly used for calculation and statistical testing, such as financial statement indicators, market data, holdings, credit lines, transaction records, and risk limits.

[0017] Unstructured text data refers to text information that uses natural language as the main medium and whose fields are not fixed, such as announcements, news, research reports, public opinion, contract terms, conference call minutes, and regulatory notices.

[0018] Among them, event evidence processing is the process of extracting, normalizing and encapsulating structured / unstructured inputs into "event + evidence", so that each event carries traceable information such as source, timestamp and credibility.

[0019] Among them, event evidence data is a structured record collection after event evidence processing, which includes at least fields such as event type, subject, timestamp, intensity / level, and evidence source and credibility.

[0020] Among them, entity identification data is the identification information used to uniquely or standardizedly locate financial entities, such as unified entity key, institution code, securities code, account number, product code, or natural person identifier.

[0021] Among them, entity relationship data describes the connections and exposed relationships between entities, such as equity / control relationships, guarantee and pledge relationships, counterparty relationships, fund transfer relationships, supply chain relationships, etc.

[0022] Among them, event type data is a label / enumeration information that classifies and encodes risk-related events, and is used to uniformly represent the semantics of events (such as pledge trigger, rating downgrade, liquidity shortage, negative public opinion, etc.).

[0023] Among them, graph structure fusion is a process of mapping subjects, events and relationships to nodes and edges of a graph, and aligning, connecting and merging them under time constraints and evidence weight rules.

[0024] Among them, the risk evidence graph data is a fused graph structure data object that includes subject nodes, event nodes, relationship edges / time edges and their auditable attributes such as evidence source, credibility, and timestamp.

[0025] Among them, the target subgraph data is a partial graph extracted from the risk evidence graph around a certain subject / risk type / time window, which is used to carry the minimum relevant evidence range for this reasoning and warning.

[0026] Among them, the large language model mechanism chain analysis is a structured reasoning process that uses the large language model to generate mechanism segments such as "trigger-amplification-transmission" and their evidence references under the evidence constraints of the target subgraph.

[0027] Among them, the causal mechanism chain data is the structured result output by the mechanism chain analysis, which represents the sequence of mechanism segments in the risk formation and propagation path, and binds corresponding evidence citation identifiers to each mechanism segment.

[0028] Assertion compilation is the process of translating the mechanism segments in the causal chain into executable and verifiable assertions (including fields, methods, thresholds / conditions) so that they can be verified by a machine.

[0029] The assertion test results data is a set of results obtained after performing rule / statistic / graphical calculations on testable assertions, including information such as pass / fail / insufficient evidence, test value and triggering evidence.

[0030] Among them, false alarm constraint calibration is a process of calibrating and robustly deciding on the warning triggering rules or thresholds under a given upper limit constraint on the false alarm rate, in order to control false alarms and improve output consistency.

[0031] Among them, the financial risk early warning result data is the final output early warning data object, which includes at least whether it has been triggered, the risk type, the key mechanism segment, the key assertions and evidence citations, and the audit elements used for reproduction.

[0032] Specifically, the structured data corresponding to the entities issuing financial risk warnings undergoes indicator extraction and anomaly detection to generate event candidates (e.g., "increased pledge ratio," "widening liquidity gap," "sudden change in credit spread"). Then, the unstructured text data corresponding to these entities undergoes entity identification, event extraction, and time normalization (extracting the "subject-behavior / state-impact-time" quadruple), and the source, publication time, author / media, and confidence level of the text fragments are used as evidence metadata. Structured events and text events are aligned by subject key and time window, deduplicated, and merged to generate event evidence data, which includes at least the fields of event type, subject identifier, timestamp, event intensity, evidence source, and evidence credibility.

[0033] The event evidence data undergoes subject normalization (mapping the same subject's cross-system ID, account, and product code to a unified subject key). Then, the subject relationship data (equity / guarantee / counterparties / fund transfers / supply chain, etc.) in the event evidence data is constructed into multi-type directed edges with timestamps and weights. The event type data in the event evidence data is then constructed into event nodes and connected to the subject graph with edges such as "subject-occurring event," "event-affecting event," and "event-time sequence." Simultaneously, the credibility of the evidence source, cross-source consistency, and time freshness are folded into the weight attributes of nodes / edges. Finally, the risk evidence graph data is obtained, which includes subject nodes, event nodes, relationship edges, time constraint edges, and their evidence chain attributes (source, credibility, timestamp, traceable citation).

[0034] Based on the target entity and risk type (credit / liquidity / market / compliance, etc.), target subgraph data is extracted from the risk evidence graph data (e.g., the K-order neighborhood of the entity, key guarantee chain paths, and time windows of recent high-intensity events). The subgraphs are then serialized into a "node-edge-evidence" structure and input into the large language model. Under constrained prompt templates, the large language model outputs mechanism chains. These chains are organized as "trigger mechanism segments—amplification mechanism segments—transmission mechanism segments," and each mechanism segment must be bound to an evidence reference identifier in the target subgraph (e.g., referencing a specific event node ID, edge ID, or evidence fragment ID). In cases with multiple propagation paths, the mechanism chain can output the main path and alternative paths, and assign mechanism roles to key nodes / edges, thus forming structured causal mechanism chain data.

[0035] Each mechanism segment in the causal chain data is translated into a testable assertion (assertion content + required data fields + testing method + pass condition). For example, "liquidity stress" is compiled into a rule assertion "short-term liabilities / realizable assets exceed the threshold and funding costs rise," "risk transmission" is compiled into a graph structure assertion "the weight of key edges in the guarantee chain increases and the interest rate spread of related entities widens synchronously," and "textual risk escalation" is compiled into a statistical assertion "the risk wording count feature increases significantly within the scrolling window." The rule engine, statistical testing module, and graph computing module are called to perform machine testing on the assertions, and the pass / fail / insufficient evidence, test value, threshold comparison results, and trigger evidence citations for each assertion are output, summarizing to form assertion testing result data (which may include key assertion markers and failure reason classifications).

[0036] Assertion test results are aggregated into calibrable risk evidence vectors (e.g., key assertion pass rate, assertion reliability weighted score, evidence chain integrity, propagation path sensitivity, etc.). Risk trigger indicators are then formed by combining the path contribution of the risk evidence graph data with the importance of key segments in the causal mechanism chain data. The trigger threshold is calibrated under a given upper limit on the false positive rate (e.g., selecting a set of thresholds / trigger surfaces that meet the upper limit based on historical backtesting samples). Furthermore, the threshold and evidence path can be perturbed using a "stability bifurcation" method. Only when the warning is stably triggered in most bifurcation scenarios is "triggered" output; otherwise, "downgrade / observation / complementation request" is output. Finally, financial risk warning result data is generated, including at least whether the warning was triggered, risk type, window period, key mechanism segments, key assertions and evidence citations, and audit elements for regulatory / reproducible analysis (input subgraph version, assertion test details, calibration parameter version, etc.).

[0037] In the aforementioned reasoning method of a large language model for financial risk early warning, structured financial data and unstructured text data are uniformly processed into event evidence and a risk evidence graph with evidence source, credibility, and timestamp is constructed. This enables the traceable fusion of risk information under subject relationships and time constraints. The large language model then generates a causal mechanism chain that can be bound to evidence citations on the target subgraph, and further asserts and compiles the mechanism chain into a machine-verifiable set of assertions to output assertion verification results. This transforms the risk explanation of "relevance narrative" into a "falsifiable and reproducible" reasoning process, significantly reducing false positives and false negatives caused by the illusion of the large language model. Finally, based on the assertion verification results, the risk evidence graph and causal mechanism chain are calibrated to constrain false positives, making the early warning trigger threshold controllable within a given upper limit of false positive rate. The output includes early warning results containing key evidence, key mechanism segments, and key assertions, thereby improving the robustness, consistency, and regulatory auditability of risk early warning, and thus increasing the accuracy and lead time of early warning while reducing the cost of manual review.

[0038] In an exemplary embodiment, a large language model mechanism chain analysis is performed on the target subgraph data in the risk evidence graph data to obtain causal mechanism chain data, including steps 202 to 206. Wherein:

[0039] Step 202: Extract the mechanism skeleton from the target subgraph data to obtain the mechanism skeleton data.

[0040] Step 204: Perform virtual intervention perturbation on the key node data or key edge data in the mechanism skeleton data to obtain mechanism fingerprint data.

[0041] Step 206: Based on the mechanism skeleton data and mechanism fingerprint data, perform constrained large language model mechanism chain analysis on the target subgraph data to obtain causal mechanism chain data.

[0042] Among them, the reverse mechanism skeleton extraction is a process of filtering and trimming the key structure required for minimum explanation by taking the risk outcome endpoint as the starting point for backtracking along the time and transmission relationship of the target subgraph.

[0043] Among them, the mechanism skeleton data is a set of subgraph structure data obtained by reverse extraction, which includes key nodes, key edges and their evidence reference information corresponding to the trigger / amplify / transfer slots.

[0044] Among them, key node data are node data that play a decisive role in the formation or spread of risk in the framework of the mechanism, and usually have high centrality / bridging or carry high-intensity event evidence.

[0045] Among them, key edge data is edge data that plays a decisive role in the risk transmission path in the mechanism framework, and usually corresponds to key connections or key time constraints such as guarantees / counterparties / funding chains.

[0046] Virtual intervention perturbation is a process of deleting, weakening, replacing, or weighting key nodes / edges without triggering real business operations in order to construct a comparative structure and observe the sensitivity of the mechanism.

[0047] Among them, mechanism fingerprint data is feature data formed by aggregating the differences in key indicators before and after virtual intervention and perturbation. It is used to characterize which combinations of structures and evidence are invariant or sensitive to the establishment of the mechanism.

[0048] Among them, the constrained large language model mechanism chain analysis drives the generation of mechanism chains by the large language model under the constraints of mechanism skeleton slots and mechanism fingerprint consistency, so that mechanism segment selection and evidence citation must be mapped to traceable graph elements of the target subgraph.

[0049] Specifically, the server executing the program determines the "risk outcome endpoint" event node (e.g., default, liquidity depletion, forced liquidation trigger, sharp widening of credit spreads, etc.) in the target subgraph data as the backtracking starting point, and performs a reverse traversal along the temporal constraint edges and transmission relationship edges using its timestamp as the anchor point, collecting candidate nodes and candidate edges that can reach the endpoint within a preset time window. The minimum interpretation criterion is introduced to prune the candidate set, for example, prioritizing the retention of nodes and edges that can cover more backtracking paths and have a smaller total cost (edge ​​weight / evidence unreliability / path length penalty), and the retained structure is slotted and labeled according to "trigger slot, amplification slot, and transmission slot" (e.g., trigger slot corresponds to abnormal event nodes, amplification slot corresponds to leverage / liquidity amplification edges, and transmission slot corresponds to guarantee chain / counterparty chain edges), thereby obtaining the mechanism skeleton data of the constraint skeleton.

[0050] Based on the key node or edge data in the mechanism skeleton data (e.g., nodes / edges with high centrality, strong bridging, located on multiple key backtracking paths, or nodes / edges corresponding to key assertions and high-intensity event evidence), a set of standardized perturbation operators is applied to construct a comparative skeleton. These perturbations may include deleting key edges, reducing edge weights, replacing evidence sources, obscuring node evidence, or changing event intensity levels. Without altering other structures, the differences in risk outcome endpoint reachability, critical path coverage, path cost distribution, and key assertion satisfiability of the skeleton before and after perturbation are calculated. These differences are then aggregated into a "mechanism invariance / sensitivity" feature vector or matrix, i.e., mechanism fingerprint data.

[0051] The target subgraph data is serialized into node-edge-evidence sequences as input, and the mechanism skeleton data is provided to the large language model in the form of "slot constraints" (e.g., the mechanism chain must contain at least two segments in triggering / amplification / transmission, and each segment can only be selected from candidate nodes / edges in the corresponding slot of the mechanism skeleton data). Simultaneously, the mechanism fingerprint data is transformed into "evidence consistency / sensitivity constraints" (e.g., priority is given to critical path evidence with high invariance fingerprints, and evidence combinations that conflict with fingerprints are prohibited). During the decoding phase, hard or soft constraints are applied to the output: hard constraints enforce mechanism segment mapping and evidence reference binding, while soft constraints penalize inconsistent evidence and reduce its probability of selection. The final output is structured causal mechanism chain data, including the mechanism segment sequence, the corresponding graph element reference (node / edge ID) for each segment, evidence reference identifiers, and optional mechanism strength / confidence labels.

[0052] In this embodiment, the target subgraph data is first subjected to reverse mechanism skeleton extraction to obtain the minimum explanatory structure covering the endpoints of risk results. Then, virtual intervention and perturbation are performed on key nodes or key edges in the skeleton to form mechanism fingerprints. This drives the large language model to perform constrained mechanism chain analysis in the manner of "structural skeleton constraint + invariant fingerprint constraint". This enables the output causal mechanism chain to be clearly bound to the key evidence path in the graph and to have falsifiability and robustness. This significantly reduces the mechanism illusion and misleading interpretation generated solely by textual correlation, improves the traceability, reproducibility and consistency of risk mechanism chains, and thus enhances the reliability of financial risk early warning inference results and reduces false alarms and false negatives.

[0053] In an exemplary embodiment, constrained large language model mechanism chain analysis is performed on the target subgraph data based on the mechanism skeleton data and mechanism fingerprint data to obtain causal mechanism chain data, including steps 302 to 308. Wherein:

[0054] Step 302: Perform constraint compilation on the mechanism skeleton data and mechanism fingerprint data to obtain mechanism chain constraint data.

[0055] Step 304: Perform counterexample-driven disabled fragment mining on the target subgraph data to obtain disabled mechanism fragment data.

[0056] Step 306: Based on the mechanism chain constraint data and the disabled mechanism fragment data, perform constrained large language model mechanism chain decoding analysis on the target subgraph data to obtain candidate causal mechanism chain data.

[0057] Step 308: Constraints are applied to the candidate causal mechanism chain data to obtain the causal mechanism chain data.

[0058] Among them, constraint compilation is the process of converting the consistency requirements of the slot structure of the mechanism skeleton and the mechanism fingerprint into decodeable executable hard and soft constraint rules.

[0059] Among them, the mechanism chain constraint data is a set of constraint representations obtained by constraint compilation, including data structures such as slot mapping constraints, sequential / reachable constraints, evidence consistency constraints and their masks, state transition or compatibility matrices, etc.

[0060] Among them, the counterexample-driven disabled fragment mining is a process of systematically discovering the smallest erroneous structure in the target subgraph that would lead to constraint conflicts or fingerprint inconsistencies, and abstracting it into a process that can match a disabled template.

[0061] Among them, the prohibited mechanism fragment data is the "non-generable fragment" template data that has been mined and normalized, representing the prohibited mechanism segment sequence, evidence citation combination, or mechanism segment-evidence alignment combination.

[0062] Among them, the constrained large language model mechanism chain decoding analysis is to perform large language model decoding under the joint control of mechanism chain constraints and prohibited fragments, so that the generation process can meet the slot, sequence, evidence consistency and avoid prohibited templates in real time.

[0063] Among them, the candidate causal mechanism chain data is a set of one or more mechanism chain candidates generated in the constrained decoding stage. Each candidate contains a mechanism segment sequence and its corresponding graph element reference and evidence reference identifier.

[0064] Among them, constraint satisfaction pruning is a process of verifying the constraints of candidate causal mechanism chains and deleting, replacing or rearranging the non-compliant paragraphs to filter out the final causal mechanism chain that satisfies the constraints.

[0065] Specifically, the slots (trigger / amplification / transmission) and their candidate node / edge sets in the skeleton data are converted into slot mapping constraints (e.g., "trigger segments can only be selected from the candidate set of trigger slots, and must contain at least one trigger segment"). The temporal order edges and transmission relationship edges in the skeleton data are converted into order constraints / reachability constraints (e.g., "trigger segments must be earlier than amplification segments, and amplification segment nodes can reach transmission segment nodes in the graph"). At the same time, the invariance / sensitivity features in the mechanism fingerprint data are compiled into evidence consistency constraints (e.g., "evidence references on high invariance paths must be satisfied first, and it is forbidden to select a set of evidence combinations that conflict with the fingerprint at the same time"). Finally, the above constraints are uniformly encoded into mechanism chain constraint data that can be executed by the decoder (which can be represented as slot masks, state transition tables, evidence compatibility matrices, or constraint logic rule sets).

[0066] "Conflict candidates" (e.g., mechanism segment-evidence alignment combinations that would lead to temporal inconsistencies, accessibility breaks, or incompatible evidence fingerprints) are generated on the target subgraph data using mechanism chain constraint data, and these conflict candidates are used as counterexamples. Then, the smallest structural fragments capable of stably reproducing conflicts (e.g., the shortest contradiction chain, the smallest unsatisfiable subset, or the smallest subgraph pattern covering the conflict) are extracted from the counterexamples and expanded into "closure counterexamples" (expanded along temporal / propagational edges to the boundary where incompatibility remains). Finally, the closure counterexamples are normalized and encoded into matchable templates (e.g., "mechanism segment sequence templates, evidence citation templates, or mechanism segment-evidence alignment templates") to obtain disabled mechanism segment data.

[0067] After serializing the target subgraph data, a structured context input model is obtained (including node / edge attributes, evidence reference identifiers, timestamps, and credibility, etc.). Mechanism chain constraint data is injected into the decoding control (e.g., opening different candidate sets for different slots, restricting the next mechanism type according to the state transition table, and penalizing candidates that do not meet the evidence consistency constraint). At the same time, a disabled mechanism fragment data matcher is enabled to perform online detection of decoding prefixes. Once a prefix matches a disabled template, it is immediately pruned or forced to fall back. Under the combined effect of the above hard constraints (mask / state machine / disabled matching) and soft constraints (fingerprint consistency penalty), the model generates several candidate mechanism chains that satisfy the constraints, and binds the corresponding graph element reference and evidence reference identifier to each mechanism segment, thereby obtaining candidate causal mechanism chain data.

[0068] For each candidate causal mechanism chain, a constraint review is performed (checking slot coverage, temporal sequence, graph reachability, evidence consistency, and whether prohibited segments are hit), and a corresponding constraint-satisfied trajectory or violation list is generated. Candidate causal mechanism chains that do not meet hard constraints are deleted or reverted (e.g., deleting violating mechanism segments, replacing them with alternative nodes / edges in the same slot, or rearranging the order of mechanism segments to meet temporal constraints). Candidates that only violate soft constraints are downgraded or partially replaced. After constraint review, the set of candidate causal mechanism chains that meet the constraints is selected based on a comprehensive score (e.g., fingerprint consistency score, evidence credibility weighted score, path coverage, and simplicity) to produce the optimal or Top-K output, forming the final causal mechanism chain data.

[0069] In this embodiment, by compiling the mechanism skeleton and mechanism fingerprint prior constraints into executable mechanism chain constraints, and mining counterexample-driven disabled mechanism fragments before decoding to explicitly characterize "ungenerate error combinations," the large language model is simultaneously subject to joint constraints of slot mapping, sequence order, evidence consistency, and disabled templates during the constrained decoding stage, suppressing mechanism illusions and evidence mismatches from the source. Furthermore, by using constraint satisfaction pruning to perform hard constraint verification and local repair on candidate mechanism chains, it is ensured that the final output causal mechanism chain is consistent with the key evidence path in the target subgraph and is traceable, reproducible, and falsifiable, thereby significantly reducing the risk of false positives and false negatives and improving the consistency and robustness of financial risk warning inference results.

[0070] In an exemplary embodiment, constrained large language model mechanism chain decoding analysis is performed on the target subgraph data based on mechanism chain constraint data and disabled mechanism fragment data to obtain candidate causal mechanism chain data, including steps 402 to 406. Wherein:

[0071] Step 402: Perform decoding control structure processing on the mechanism chain constraint data and the disabled mechanism fragment data to obtain decoded control data.

[0072] Step 404: Prefix counterexample detection is performed on the target subgraph data to obtain prefix disabled mapping data.

[0073] Step 406: Perform adversarial evidence projection by mechanism fingerprint inversion on the evidence path data in the target subgraph data to obtain evidence projection cost field data.

[0074] Step 408: Based on the decoding control data, prefix disabling mapping data, and evidence projection cost field data, perform reversible backtracking decoding of the target subgraph data with potential field constraints to obtain the initial candidate mechanism chain data.

[0075] Step 410: Perform candidate solidification processing on the initial candidate mechanism chain data to carry constraint trajectories, and obtain candidate causal mechanism chain data.

[0076] Among them, evidence path data is a sequence of traceable evidence links formed by connecting nodes and edges in the target subgraph according to time constraints or transmission relationships. It is used to represent the potential propagation path from trigger to result and its evidence reference.

[0077] Among them, the adversarial evidence projection of mechanism fingerprint inversion is a process of constructing adversarial evidence mapping that conflicts with the consistency characteristics of the mechanism fingerprint, and projecting candidate evidence paths into the adversarial space to quantify the degree of deviation from the fingerprint consistency.

[0078] Among them, the evidence projection cost field data is the cost distribution data that assigns continuous penalty costs to the combination of "mechanism segment - evidence path - alignment method" and organizes them into a matrix / tensor. It is used to reweight or prune inconsistent evidence alignment during decoding.

[0079] Prefix counterexample detection is a process of probing possible mechanism chain prefixes before or early in the generation of the mechanism chain and identifying prefixes that will inevitably trigger disabled segments or hard constraint conflicts.

[0080] Among them, the prefix disabling mapping data is the index data that establishes a mapping between the mechanism chain prefix that will be determined to be non-extendable and its disabling reason, disabling template identifier, or the next candidate set that prohibits extension.

[0081] Among them, the reversible backtracking decoding of potential energy field constraints is a decoding process that searches for mechanism chain sequences under constraints with the potential energy / cost function as the guide, and performs backtracking and replacement when the disabled prefix is ​​triggered or the hard constraint / cost threshold is violated.

[0082] The initial candidate mechanism chain data is a set of mechanism chain candidates that satisfy the main constraints, have bound graph elements, and have evidence references, obtained by reversible backtracking decoding of potential energy field constraints.

[0083] Among them, the candidate solidification process carrying constraint trajectories is a process of recording the constraint satisfaction and backtracking pruning trajectory of each initial candidate mechanism chain, and filtering, truncating or eliminating candidates accordingly to form an auditable and stable output.

[0084] Specifically, the slot mapping constraints (trigger / amplification / propagation slots and candidate node / edge sets) of the mechanism chain constraint data are transformed into slot masks and slot filling rules. The sequence constraints and reachability constraints of the mechanism chain constraint data are transformed into executable state transition tables / finite state machines (limiting the type and legal order of the next mechanism segment). Furthermore, the evidence consistency constraints of the mechanism chain constraint data are transformed into evidence compatibility matrices or "allow / penalty" tables. Simultaneously, the disabled mechanism fragment data is normalized into an online matching disabled template matcher (e.g., templates based on mechanism segment sequences, evidence reference sequences, or alignment pairs), and the corresponding matching state representation is output. All of the above data structures are uniformly encapsulated into decoding control data, enabling it to be directly driven during the decoding process in the form of "mask constraints, state machine transitions, template matching, and scoring penalties."

[0085] Based on the available slot masks and state transition constraints in the decoded control data, a batch of probe prefixes covering common opening patterns are constructed (e.g., short prefix combinations of "trigger segment → amplification segment", or short sequence combinations of "trigger segment + evidence reference"). These prefixes are then mapped to candidate graph element references and evidence reference candidates in the target subgraph data. A disabling template matcher is then used to perform online matching on each probe prefix, identifying which prefixes will inevitably hit the disabling mechanism fragment or cause constraints to become unsatisfactory when further expanded. Finally, "prefix → disabling reason / disabling template ID / next step candidate set for disabling expansion" is encoded into prefix disabling mapping data.

[0086] Evidence paths related to candidate mechanism segments are enumerated or extracted from the subgraph data (e.g., several key propagation paths from the subject to the risk endpoint, key event chains, and guarantee / counterpartie chain paths). Based on the mechanism fingerprint data, invariant features that "must remain consistent" and sensitive features that "are prone to conflict" are determined. Then, adversarial projections are constructed based on the above data: specifically, for each evidence path, alternative paths or alternative evidence that conflict with the fingerprint are generated (e.g., replacing with low-credibility sources, obscuring key node evidence, changing the weight of key edges in the path). The difference between the original path and the adversarial projection in the fingerprint feature space is calculated. Finally, this difference is transformed into a cost (penalty) for each "mechanism segment - evidence path - alignment method" and organized into a tensor / matrix form, i.e., evidence projection cost field data.

[0087] The available node / edge / evidence reference candidates in the target subgraph data are loaded into the decoding candidate pool. An extensible set of "current mechanism chain prefix state → next optional mechanism segment type and optional graph element reference" is defined using the slot mapping constraint mask and sequential constraint state transition rules in the decoding control data. Each time the extended mechanism chain prefix is ​​executed, the prefix disabling mapping data is first queried to determine whether the current prefix and its extensible set will hit a disabled mechanism segment or trigger an unsatisfiable constraint prefix pattern. If it does, the extended branch is pruned and the process reverts to the previous replaceable slot. For unpruned extended branches, the cost penalty for the candidate "mechanism segment—evidence path—alignment method" is calculated based on the evidence projection cost field data. This cost penalty is then combined with the language model's generated score to form the potential value of the branch. Simultaneously, the lower bound of the potential value is calculated from the evidence projection cost field data based on the minimum possible cost of unfilled slots for look-ahead pruning. When the branch potential value or lower bound exceeds the threshold, or when the hard constraints (slot / order / reachability) of the decoding control data are violated, a reversible backtracking is performed to roll back the decoding state and replace the candidate graph element references or candidate evidence alignment. The above search process of "controlled expansion - prefix disabling pruning - cost field penalty - potential lower bound pruning - reversible backtracking" is repeated to output several initial candidate mechanism chain data that satisfy the decoding control data constraints, avoid the disabled prefixes indicated by the prefix disabling mapping data, and have a low cost under the evidence projection cost field data. These are used as the initial candidate mechanism chain data.

[0088] For each candidate chain in the initial candidate mechanism chain data, corresponding constraint trajectory data is generated. This constraint trajectory data records at least the candidate chain's slot mask hit status, state transition path, hard constraint satisfaction record, and query hit status, pruning count, backtracking count, and backtracking position for the decoding control data during the decoding process. Simultaneously, the cost trajectory (including the evidence path alignment cost and total cost selected for each mechanism segment) of each candidate chain is accumulated and recorded on the evidence projection cost field data, and this trajectory is encapsulated along with the candidate chain's graph element references and evidence reference identifiers in the target subgraph data. A solidification judgment is performed on each candidate chain based on the constraint trajectory data: if any hard constraint is not satisfied, a disabled prefix is ​​hit, or the total cost exceeds a threshold, the candidate chain is eliminated or truncated; otherwise, it is marked as a solidifiable candidate. Finally, solidifiable candidates are filtered and retained according to the sorting rules of "constraint satisfaction priority, lower total cost priority, fewer backtracking count priority," and the retained candidate chains, along with their constraint trajectory data, graph element references, and evidence reference information, form the candidate causal mechanism chain data.

[0089] In this embodiment, prefix disabling mapping data is generated by first detecting prefix counterexamples in the target subgraph data to intercept prefix combinations that inevitably lead to constraint conflicts in the early stages of generation. Then, adversarial evidence projection is generated by the fingerprint inversion of the evidence path in the target subgraph to form evidence projection cost field data to apply continuous penalties to inconsistent evidence alignment. Thus, in the decoding stage, the decoding control data, together with the prefix disabling mapping data and the evidence projection cost field data, are used to implement reversible backtracking decoding of potential field constraints, resulting in initial candidate mechanism chain data that meets hard constraints and has lower costs. Furthermore, candidate solidification processing carrying constraint trajectories is used to retain candidate causal mechanism chain data with clear constraint-satisfying trajectories that are auditable and robust. This significantly reduces illusions, evidence mismatches, and invalid search branches in the generation of mechanism chains in large language models, improves the traceability, stability, and generation efficiency of candidate mechanism chains, thereby reducing the risk of false positives and false negatives and improving the reliability of financial risk warning inference results.

[0090] In an exemplary embodiment, counterexample-driven disabled fragment mining is performed on the target subgraph data to obtain disabled mechanism fragment data, including steps 502 to 508. Wherein:

[0091] Step 502: Perform conflict explicit processing on the target subgraph data and mechanism chain constraint data to obtain conflict candidate set data.

[0092] Step 504: Perform minimum cut counterexample extraction on the conflict candidate set data to obtain counterexample seed fragment data.

[0093] Step 506: Based on the counterexample seed fragment data, perform adversarial expansion closure processing on the target subgraph data to obtain counterexample closure fragment data.

[0094] Step 508: Perform disabling normalization encoding on the counterexample closure fragment data to obtain disabling mechanism fragment data.

[0095] Among them, conflict explicit processing is a process of performing consistency detection on candidate mechanism segment combinations and evidence alignment combinations in the target subgraph based on mechanism chain constraints, and explicitly marking the combinations that violate the constraints.

[0096] Among them, the conflict candidate set data is a set of candidate combinations marked as having constraint conflicts, including conflict location, conflict type, and corresponding graph elements and evidence reference identifiers.

[0097] Minimum cut counterexample extraction is a process that uses minimum cut / minimum cut set to extract the minimum counterexample structure that can reproduce the conflict in the smallest scale from the conflict candidate set.

[0098] Among them, the counterexample seed fragment data is the minimum conflict fragment data extracted from the minimum cut counterexample, which usually consists of a small number of key mechanism segments-evidence alignment pairs or the shortest contradiction chain.

[0099] Among them, the adversarial expansion closure processing is a process of expanding along the time edge / transmission edge / evidence dependency edge with the counterexample seed fragment as the core, and retaining only the expansion results that still maintain constraint incompatibility to form a closure fragment.

[0100] Among them, the counterexample closure fragment data is counterexample fragment data with closure properties obtained by adversarial expansion closure processing. Once it appears, it is highly likely to lead to unsatisfactory constraints or evidence mismatch in subsequent generation.

[0101] Among them, disabling normalization encoding involves normalizing and encoding the counterexample closure fragment into a matchable disabled template (sequence / combination / alignment pattern).

[0102] Specifically, the mechanism chain constraint data is decomposed into a set of computable constraint terms (such as slot mapping constraints, sequence constraints, graph reachability constraints, and evidence consistency constraints). Candidate mechanism segments, candidate evidence references, and candidate mechanism segment-evidence alignment pairs from the target subgraph data are then used to generate a candidate combination space. Each candidate combination is evaluated for consistency and tagged with conflicts based on its constraint terms. For example, conflicts such as temporal sequence conflicts (e.g., the trigger segment being later than the amplification segment), structural conflicts (e.g., the corresponding node of the mechanism segment being unreachable on the graph), and evidence conflicts (e.g., the evidence reference combination being incompatible with the fingerprint compatibility matrix) are collected. The conflicting combinations, along with their conflict locations (mechanism segment index / evidence ID / graph element ID) and conflict type codes, are then aggregated to form a conflict candidate set data.

[0103] The conflict candidate set data is mapped to a "satisfiable-unsatisfiable" cut structure. For example, candidate mechanism segments / evidence alignment pairs are treated as nodes in a graph, and their dependencies (same slot competition, order dependency, same evidence mutual exclusion, path reachability dependency, etc.) are treated as edges. The "set satisfying constraints" and the "set triggering conflict" are mapped to the source and sink nodes, respectively. Minimum cuts or minimum cut sets are used to find the minimum cut set that ensures connectivity from the "satisfiable side" to the "conflict side," resulting in the smallest possible counterexample structural fragment sufficient to reproduce the conflict. This counterexample seed fragment data typically represents a set of minimum mechanism segment-evidence alignment pairs or a shortest chain of contradictions / minimum unsatisfiable subset, serving as the starting point for closure expansion.

[0104] Using counterexample seed fragment data as the core, the algorithm expands outward from the target subgraph data along temporal constraint edges, propagation relationship edges, or evidence citation dependency edges, gradually incorporating adjacent nodes and edges that are causally related, propagation dependent, or evidence-coupled with the counterexample seed. During the expansion process, an "adversarial preservation" judgment is performed on each expanded fragment, i.e., checking whether the fragment is still unsatisfiable or still stably triggers similar conflicts under the mechanism chain constraint data. The expansion is only retained if the conflict is still maintained; otherwise, the expansion branch is regressed. The expansion terminates when further expansion no longer increases conflict stability or the fragment has reached a preset size limit, resulting in counterexample closure fragment data with closure properties under constraints. The characteristic of this data is that once it appears in decoding, it has a high probability of causing subsequent generation to inevitably fail or evidence mismatch.

[0105] The details in the counterexample closure fragment data that are strongly bound to specific subjects / timestamps are normalized (e.g., specific node IDs are replaced with node types and slot labels, specific evidence IDs are replaced with evidence types and confidence intervals, and specific time differences are replaced with relative order constraints). The normalized structure is then encoded into a matchable disallowed template. Disallowed templates can include mechanism segment sequence templates (e.g., the illegal order of "trigger segment A → conduction segment B → amplification segment C"), evidence citation combination templates (e.g., combinations of "the same mechanism segment simultaneously citing mutually exclusive sources"), and mechanism segment-evidence alignment templates (e.g., the pattern of "a slot mechanism segment citing a certain type of low-confidence evidence and connecting to an unreachable path"). A matching key and matching state machine / rule expression are generated for each template to form the disabled mechanism fragment data.

[0106] In this embodiment, conflict manifestation is performed on the target subgraph data and mechanism chain constraint data to explicitly expose the mechanism segment-evidence combination of "unsatisfiable constraints". Then, minimum cut counterexample extraction is used to obtain counterexample seed fragments that can stably reproduce the conflict with the smallest scale. On this basis, adversarial expansion closure is used to obtain counterexample closure fragments that have a high probability of causing failure once they appear. Finally, these are normalized and encoded into disabled mechanism fragment data that can be matched online. This allows for early pruning and prohibition of generating high-risk erroneous combinations in the subsequent constrained decoding process, significantly reducing the number of invalid searches and constraint conflict backtracking, reducing false positives and false negatives caused by evidence mismatch and mechanism illusion, and improving the robustness, traceability and overall reasoning efficiency of causal mechanism chain generation.

[0107] In an exemplary embodiment, reverse mechanism skeleton extraction is performed on the target subgraph data to obtain mechanism skeleton data, including steps 602 to 608. Wherein:

[0108] Step 602: Perform time-reverse causal candidate seeding on the risk outcome endpoint node data in the target subgraph data to obtain the backtracking seed set data.

[0109] Step 604: Perform evidence credibility folding and weighting on the backtracking seed set data to obtain weighted backtracking frontier data.

[0110] Step 606: Perform multi-objective minimum interpretation reduction on the weighted backtracking frontier data to obtain skeleton candidate subgraph data.

[0111] Step 608: Perform strong connectivity pruning on the candidate subgraph data of the skeleton to obtain the mechanism skeleton data.

[0112] Among them, the risk outcome endpoint node data is the node data that represents the final risk outcome state or event in the target subgraph, and is used as the starting anchor point for reverse backtracking.

[0113] Among them, the time-reverse causal candidate broadcasting is a process of extending upstream candidate nodes and candidate edges in reverse along the time sequence constraint edges and transmission relationship edges, starting from the risk outcome endpoint.

[0114] Among them, the backtracking seed set data is the set of candidate nodes and candidate edges obtained by time-reverse seeding, and carries backtracking metadata such as backtracking distance, edge type and evidence reference.

[0115] Among them, the evidence credibility folding weighting is to fold credibility elements such as the credibility of evidence source, cross-source consistency, and time freshness into node / edge weights to support the subsequent screening process.

[0116] Among them, the weighted backtracking frontier data is a weighted candidate frontier data structure formed after being weighted by evidence credibility folding, which is used to guide the selection priority in the next reduction step.

[0117] Among them, the multi-objective minimum interpretation reduction process is a process of pruning and selecting the weighted backtracking frontier to obtain the minimum interpretation structure under the joint objectives of maximizing interpretation coverage and minimizing total cost.

[0118] Among them, the skeleton candidate subgraph data is the candidate subgraph structure data obtained by multi-objective minimum interpretation reduction, which includes key nodes and key edges that have interpretive contributions to the endpoints of risk outcomes.

[0119] Among them, strong connectivity pruning is a process of decomposing strongly connected components to remove redundant nodes / edges that are irrelevant to the endpoints or do not contribute to connectivity, and extracting compact structures from the skeleton candidate subgraph.

[0120] Specifically, risk outcome endpoint nodes (such as default event nodes, forced liquidation trigger nodes, liquidity depletion nodes, etc.) are extracted from the target subgraph data, and their timestamps are read as backtracking anchors. Then, a hierarchical traversal is performed in the reverse direction along the time-sequence constraint edges and risk transmission edges in the target subgraph data. Upstream nodes and upstream edges that are before the anchors and meet the preset time window are added to the candidate set layer by layer. During the propagation process, allow / prohibit rules are set for different edge types (such as prioritizing backtracking along the guarantee chain, funding chain, and counterparty chain, and restricting irrelevant edges). For each layer of candidates, the shortest backtracking distance to the endpoint, the edge type sequence, and the evidence reference identifier are recorded, thereby forming a backtracking seed set data containing candidate nodes, candidate edges, and their backtracking metadata.

[0121] The backtracking seed set data is read one by one, including candidate node data and candidate edge data. For each candidate node / edge data, the corresponding evidence source data, evidence credibility data, timestamp data, and cross-source consistency score data are retrieved from the target subgraph data based on the evidence citation identifier associated with it. The evidence credibility data and cross-source consistency score data are fused and folded to obtain basic credibility weight data. The time difference between the timestamp data and the anchoring time of the risk result endpoint node data is calculated as the time freshness weight data. Simultaneously, the backtracking distance data and edge type data of the candidate node / edge data are extracted from the backtracking seed set data. The backtracking distance data is converted into distance decay weight data, and the edge type data is mapped to relationship importance weight data. The basic credibility weight data, time freshness weight data, distance decay weight data, and relationship importance weight data are combined according to a preset folding function to form a comprehensive weight data. This comprehensive weight data is written back to the corresponding candidate node data and candidate edge data in the backtracking seed set data as node weight data or edge weight data. The seed set data for backtracking is sorted and layered based on the comprehensive weight data, forming a queue or set structure containing "candidate node data / candidate edge data - node weight data / edge weight data - evidence source data - evidence credibility data - timestamp data - cross-source consistency score data - backtracking distance data - edge type data", which serves as the weighted backtracking frontier data.

[0122] This approach optimizes both "explanatory power" and "simplicity." Explanatory power is represented by metrics such as the backtracking path coverage of risk outcome endpoints, the number of high-intensity events covered, and the number of key relationship edges covered. Simplicity is represented by the number of nodes / edges in the subgraph and the total weighted cost (the sum of node / edge weights or path cost). Under this multi-objective constraint, candidate nodes and edges in the weighted backtracking front data are selected and pruned. This can be achieved through iterative path coverage selection (prioritizing nodes / edges that maximize coverage gain and have the lowest unit cost) or Pareto front screening (retaining subgraph candidates that cannot be dominated by coverage and cost). After each round of selection, the coverage gain and cost margins are recalculated until the coverage threshold is met or the size limit is reached. Ultimately, this yields skeleton candidate subgraph data that simultaneously possesses high explanatory coverage and low total cost.

[0123] The candidate subgraph data is treated as a directed graph and strong connected component decomposition (e.g., based on Tarjan's algorithm or Kosaraju's algorithm) is performed to identify strongly connected components containing risk outcome endpoint nodes and necessary components that are reachable from them. Then, isolated components, redundant edges, and dangling nodes that are not located in the strongly connected components of the endpoints and do not contribute to endpoint reachability and mechanism slot coverage are deleted. The retained structure is then slotted (classified and labeled with trigger / amplification / propagation candidates) to obtain mechanism skeleton data that is compact, traceable, interpretable, and strongly coupled with risk outcome endpoints.

[0124] In this embodiment, key upstream candidates are focused by time-backward broadcasting with risk outcome endpoints as anchors. Redundant paths are removed by combining evidence credibility folding weighting and multi-objective minimum interpretation reduction. Finally, the mechanism skeleton data with a compact structure and strong coupling with the endpoints is formed through strong connectivity pruning, thereby improving the constrainability and traceability of subsequent mechanism chain generation and reducing false positives and false negatives.

[0125] In an exemplary embodiment, based on the assertion test results data, false alarm constraint calibration is performed on the risk evidence graph data and the causal mechanism chain data to obtain financial risk early warning result data, including steps 702 to 708. Wherein:

[0126] Step 702: Perform assertion falsifiability spectrum decomposition on the assertion test result data to obtain assertion reliability spectrum data.

[0127] Step 704: Based on the assertion reliability spectrum data, perform risk propagation tensor mapping on the risk evidence graph data and causal mechanism chain data to obtain propagation sensitivity tensor data.

[0128] Step 706: Perform a threshold inversion analysis driven by the false alarm upper limit on the propagation sensitivity tensor data to obtain calibration trigger surface data.

[0129] Step 708: Based on the calibration trigger surface data, perform stability bifurcation decision on the propagation sensitivity tensor data to obtain financial risk early warning result data.

[0130] Among them, assertion falsifiability spectral decomposition is a process of constructing an assertion correlation matrix and performing spectral decomposition based on the pass / fail / insufficient evidence patterns and conflict relationships in the assertion test results, in order to quantify the falsifiability and reliability of each assertion.

[0131] Among them, the assertion reliability spectrum data is a set of assertion reliability characterization data output by spectral decomposition, including assertion weight vector, reliability level and uncertainty label, etc.

[0132] Among them, risk propagation tensor mapping is a process of aligning the propagation path in the risk evidence graph with the mechanism segment of the causal mechanism chain, and encoding the path contribution as a multidimensional tensor with the support of assertion reliability weights.

[0133] Among them, the propagation sensitivity tensor data is a multidimensional contribution data structure obtained by tensor mapping, which is used to characterize the sensitivity contribution of different mechanism slots, path structures and evidence types to risk triggering.

[0134] Among them, the threshold inversion analysis driven by the false alarm cap is an analysis process that inversely derives the set of trigger thresholds that satisfy the false alarm constraint from the risk statistics derived from the propagation sensitivity tensor under the constraint of a preset false alarm rate cap.

[0135] Among them, the calibration trigger surface data is the “trigger / non-trigger” boundary representation data obtained by threshold inversion, which includes the threshold set, interface parameters and applicable condition labels.

[0136] Among them, the stability bifurcation decision is a decision-making process that involves perturbating the threshold and evidence path around the calibration trigger face and determining whether to trigger an early warning based on trigger consistency or stability kernel metric.

[0137] Specifically, assertion test results are aggregated into assertion-level feature vector data based on assertion identifiers. This assertion-level feature vector data includes at least pass / fail / insufficient evidence markers, test value / threshold difference data, failure reason category data, evidence source credibility summary data, and cross-source consistency conflict count data. An assertion similarity map or correlation matrix is ​​constructed based on the co-occurrence relationships between different assertion data within the same mechanism segment, the same evidence path, or the same subject time window. Spectral decomposition is then performed on this matrix to obtain low-dimensional spectral embedding data. This low-dimensional spectral embedding data is mapped to assertion reliability weight data according to falsifiability hierarchical rules (e.g., assertions that are "directly machine-testable and have sufficient evidence" have higher weights, while those that are "insufficient evidence or have many conflicts" have lower weights). The output is assertion reliability spectral data containing assertion weight vectors, reliability level labels, and uncertainty markers.

[0138] The causal mechanism chain data is decomposed into mechanism segment sequences and their bound graph element references and evidence reference identifiers. Key nodes, key edges, and their candidate evidence path sets corresponding to these references are retrieved from the risk evidence graph data. The assertion weight vector from the assertion reliability spectrum data is injected into the contribution calculation of each path in the candidate evidence path set. For each candidate evidence path, the weighted pass rate, weighted failure rate, and weighted evidence sufficiency of the assertion set it carries are calculated. The risk contribution of this path is defined as "mechanism segment influence intensity × path reachability / time sequence satisfaction × assertion reliability weighted consistency". Tensor indexes are constructed using the mechanism slot dimension (trigger / amplification / propagation), path structure dimension (path length / edge type sequence / key edge set), and evidence type dimension (structured indicators / textual evidence / cross-source consistency). The contributions of each path are written into tensor units to form propagation sensitivity tensor data.

[0139] Risk aggregation statistics (such as the overall risk score obtained by slot-weighted summation, tail contribution score, or maximum contribution score of the critical path) are generated from the propagation sensitivity tensor data to determine triggers. These statistics, combined with historical labeled samples or backtesting window statistics, yield false positive rate estimation functions at different thresholds. Under a given upper limit constraint on the false positive rate, a set of thresholds that ensure the false positive rate does not exceed the upper limit is derived, and this set is represented as a "trigger-non-trigger" interface in the multidimensional statistical space. When multiple risk types or differences exist among multiple entities, the corresponding interfaces can be learned separately and layered conditions (such as industry / rating / liquidity level) can be added. The final output is calibrated trigger surface data containing the threshold set, interface parameters, and applicable condition labels.

[0140] A set of threshold perturbation bifurcations is generated around the calibration trigger surface data in the normal and tangential directions. These bifurcations are then combined with the Top-K high-contribution paths in the propagation sensitivity tensor data to perform perturbation bifurcation on the evidence path weights or path sets, thus forming a bifurcation scenario set. For each bifurcation scenario, the propagation sensitivity tensor data is updated to scenario risk statistics according to perturbation rules, and its triggering state (triggered / not triggered) relative to the calibration trigger surface is determined, resulting in a triggering state sequence. Based on this triggering state sequence, stability metrics (such as trigger consistency ratio, minimum stable kernel region size, or minimum perturbation distance of the trigger boundary) are calculated. When the stability metric reaches a preset stability condition, an "early warning triggered" is output; otherwise, an "early warning downgrade / observation or data completion request" is output. The trigger status, corresponding risk type, key contribution paths, key mechanism segments and key assertion sets, and stability metric results are encapsulated into financial risk early warning result data.

[0141] In this embodiment, assertion reliability spectrum data is obtained by performing falsifiability spectrum decomposition on assertion test results data and then injecting it into the propagation tensor mapping of risk evidence graph and causal mechanism chain to obtain quantifiable and weighted propagation sensitivity tensor data. This transforms risk trigger determination from "narrative explanation" to "calculable propagation contribution". Then, under the constraint of false alarm upper limit, threshold inversion is performed on the propagation sensitivity tensor data to obtain calibration trigger surface data, making the warning threshold controllable for false alarms. Finally, based on the calibration trigger surface, stability bifurcation decision is implemented, and financial risk warning results are output only when the threshold and evidence perturbation still maintain triggering. This significantly reduces false alarms, suppresses noise triggering caused by unstable evidence, and improves the consistency, robustness, and auditability of warning results.

[0142] In an exemplary embodiment, risk propagation tensor mapping is performed on risk evidence graph data and causal mechanism chain data based on assertion reliability spectrum data to obtain propagation sensitivity tensor data, including steps 802 to 810. Wherein:

[0143] Step 802: Use the mechanism segment data in the causal mechanism chain data to perform slotted path anchoring on the risk evidence graph data to obtain mechanism anchoring path seed data.

[0144] Step 804: Perform spectral weight injection propagation reachability expansion on the mechanism anchoring path seed data to obtain candidate propagation path set data.

[0145] Step 806: Perform adversarial consistency resampling on the candidate propagation path set data to obtain robust propagation path subset data.

[0146] Step 808: Tensor-encode the robust propagation path subset data to obtain the initial sensitivity tensor data.

[0147] Step 810: Perform low-rank decomposition and compression on the initial sensitivity tensor data to obtain propagation sensitivity tensor data.

[0148] Among them, mechanism segment data consists of mechanism fragments organized in the causal mechanism chain according to slots such as trigger / amplification / conduction, as well as their corresponding graph element references and evidence reference information.

[0149] Among them, slot-based path anchoring is a process of mapping mechanism segments in different slots to path start-end constraints, necessary element constraints, and time sequence constraints on the risk evidence map to determine the propagation search seed.

[0150] Among them, the mechanism-anchored path seed data is a set of path seed data generated by slotted path anchoring, which includes information such as the start-end pair, path constraints and time window corresponding to each slot.

[0151] Among them, the propagation reachability expansion of spectral weight injection is a process of folding the assertion reliability spectral weights into graph node / edge weights and performing a weighted reachability search accordingly to expand the propagation path.

[0152] Among them, the candidate propagation path set data is the set of propagation paths that satisfy the anchoring constraints and are reachable under the spectral weights, obtained by propagation reachability expansion.

[0153] Among them, adversarial consistency resampling is a process of applying adversarial perturbations such as evidence replacement / masking / weight perturbation to candidate propagation paths and re-sampling according to the consistency preservation status.

[0154] Among them, the robust propagation path subset data is the propagation path subset data that remains consistent with the spectral weights under perturbation conditions after adversarial consistency resampling.

[0155] Tensor quantization encoding is an encoding process that writes path contribution values ​​into multidimensional tensor units according to the mechanism slot dimension, path structure dimension, and evidence type dimension.

[0156] Among them, the initial sensitivity tensor data is a high-dimensional contribution tensor data obtained by tensor encoding, which is used to characterize the sensitivity contribution of different slot-path-evidence combinations to risk triggering.

[0157] Among them, low-rank decomposition and compression is a process that uses the tensor low-rank approximation method to decompose the initial sensitivity tensor to reduce dimensionality, reduce storage and computational overhead, and retain the main propagation mode.

[0158] Specifically, the causal mechanism chain data is parsed into a sequence of mechanism segments organized by slot (trigger slot, amplification slot, and transmission slot). The graph element references and evidence reference identifiers bound to each mechanism segment are read as mechanism segment data. In the risk evidence graph data, the event nodes, subject nodes, or relational edges corresponding to each mechanism segment are located and mapped to path constraints (such as start-point sets, end-point sets, sets of critical edges that must be traversed, and sets of prohibited edges) and time constraints (such as a required sequence). For each mechanism segment, a set of anchoring descriptions is constructed: "start-end pair + required / optional edge type + time window." All slot anchoring descriptions are then aggregated into mechanism anchoring path seed data.

[0159] The assertion weight vector in the assertion reliability spectrum data is folded into node weights and edge weights by mapping "assertion → evidence reference → graph node / edge". This weight is then written into the corresponding node / edge attributes of the risk evidence graph data as a modulation factor for propagation cost or propagation probability. Using the start-end pair in the mechanism anchoring path seed data as constraints, a weighted reachability expansion (e.g., weighted BFS / weighted shortest path / restricted K-hop expansion) is performed along the risk evidence graph. During the expansion process, both edge type whitelist and time sequence constraints are satisfied. Branches with cumulative cost exceeding a threshold or excessively low weights are truncated. Multiple propagation paths that satisfy the anchoring constraints and are "reachable and reliable" in the sense of spectrum weights are output, forming a candidate propagation path set.

[0160] For each candidate propagation path in the selected propagation path set, a set of adversarial perturbation versions is generated. These include perturbations such as evidence replacement on low-confidence evidence edges, masking on key node evidence, or random / directional perturbations on edge weights to simulate noise and evidence uncertainty. Under each perturbation version, the spectral weight consistency score of the path is recalculated (e.g., assertion reliability weighted pass rate, cross-source consistency rate, or path contribution ranking stability), and paths whose scores remain above a threshold under most perturbations are retained as robust paths. Finally, the retained paths are resampled according to their consistency scores (e.g., paths with high consistency and covering different key edges are prioritized) to obtain a robust propagation path subset.

[0161] The system distinguishes between triggering / amplification / propagation based on mechanism slots, describes path length, edge type sequence, key edge set, or time span based on path structure, and differentiates between structured indicator evidence, textual evidence, and cross-source consistency evidence based on evidence type. Using these two definitions as an indexing system for tensors, the system calculates the path contribution value (e.g., "mechanism segment strength × path reachability × spectral weight consistency score × evidence credibility summary") for each robust path in the robust propagation path subset data. This contribution value is then written into the tensor unit corresponding to its slot, structure, and evidence type. Simultaneously, the Top-K critical path indexes can be recorded as non-zero coordinates of the sparse tensor, resulting in the initial sensitivity tensor data.

[0162] The initial sensitivity tensor data is decomposed using a low-rank tensor approximation method (such as CP decomposition, Tucker decomposition, or tensor SVD approximation) to obtain low-rank factor matrices and core tensors for each dimension. The rank parameter is selected based on the reconstruction error or energy retention rate. The original high-dimensional tensor is replaced with a low-rank representation to achieve storage and computational compression, while preserving the dominant propagation pattern and the key slot-path-evidence type interaction structure, outputting compressed propagation sensitivity tensor data.

[0163] In this embodiment, a candidate propagation path set is obtained by slotting the risk evidence graph with the mechanism segment of the causal mechanism chain and expanding it with assertion reliability spectrum weight injection, so that the propagation path selection is consistent with the mechanism semantics and assertion reliability. Then, adversarial consistency resampling is used to screen out non-robust paths that are sensitive to evidence perturbation, reducing noise propagation caused by accidental evidence. Subsequently, the contribution tensor of robust paths is quantized and encoded to form an initial sensitivity tensor, and the propagation sensitivity tensor data is obtained by low-rank decomposition and compression. This reduces dimensionality and computational overhead while retaining the main propagation mode, improves the efficiency, robustness and auditability of subsequent threshold calibration and stability decision-making, and reduces false positives and false negatives.

[0164] In an exemplary embodiment, based on the calibration trigger surface data, a stability bifurcation decision is made on the propagation sensitivity tensor data to obtain financial risk early warning result data, including steps 902 to 908. Wherein:

[0165] Step 902: Perform normal perturbation bifurcation processing on the calibration trigger surface data to obtain bifurcation scenario parameter set data.

[0166] Step 904: Based on the bifurcation scenario parameter set data, perform phase transition boundary tracking on the propagation sensitivity tensor data to obtain the bifurcation phase transition boundary data.

[0167] Step 906: Extract the stability kernel from the bifurcation phase transition boundary data to obtain stability measurement data.

[0168] Step 908: Perform robust statistical aggregation on the stability measurement data to obtain financial risk early warning results data.

[0169] Among them, the normal perturbation bifurcation processing is a process of generating multiple sets of threshold and evidence perturbation combinations along the calibration trigger surface in the normal direction to construct a controllable bifurcation scenario.

[0170] Among them, the bifurcation scenario parameter set data is the set of scenario parameters generated by bifurcation, which includes the threshold perturbation magnitude, evidence path perturbation rules and their condition labels for each scenario.

[0171] Phase transition boundary tracking is a process of detecting the transition point from triggering to non-triggering (or vice versa) in the disturbance parameter space and connecting them to form a boundary.

[0172] Among them, the bifurcation phase transition boundary data is the boundary representation data obtained by phase transition boundary tracking, which is used to characterize the boundary position and shape of the triggered phase and the non-triggered phase in the parameter space.

[0173] Among them, stable kernel extraction is the process of extracting the largest connected stable region with consistent trigger states within the region divided by the bifurcation phase transition boundary.

[0174] Among them, stability metrics are calculated from the geometric and statistical characteristics of the stable kernel, such as the stable kernel volume / area, minimum distance to the boundary, or robust radius.

[0175] Robust statistical aggregation is a process that uses robust statistics to aggregate stability measurement data and outputs early warning triggers or downgrade conclusions accordingly.

[0176] Specifically, the calibration trigger surface data is parsed into a boundary representation for trigger determination (e.g., threshold set, interface parameters, or segmentation conditions), and the corresponding local tangent plane and normal direction are located on this interface for the current risk statistic of the target subject. Multiple sets of threshold perturbation parameters (e.g., perturbation amplitude, quantile step size, or exponential step size) are generated along the normal direction, and the threshold perturbation can be combined with evidence path perturbation rules (e.g., weight perturbation amplitude, occlusion ratio, or replacement ratio for Top-K contribution paths) for encoding. Each set of "threshold perturbation parameters + evidence path perturbation parameters + applicable condition labels" is encapsulated into a bifurcation scenario entry, and the bifurcation scenario parameter set data is obtained by summarizing them.

[0177] Each bifurcation scenario parameter in the bifurcation scenario parameter set is mapped to a computable risk statistic in the propagation sensitivity tensor data. For example, the triggering threshold is adjusted based on the threshold perturbation parameter, and the corresponding path index cell in the tensor is reweighted or zeroed based on the evidence path perturbation parameter, thus obtaining the scenario risk statistic for that scenario. The triggering state (triggered / non-triggered) is calculated point-by-point along the ordered perturbation sequence of the bifurcation scenario parameters (e.g., from negative to positive perturbation), and the transition points of the triggering state (i.e., the points where the triggering state changes from non-triggered to non-triggered or vice versa) are detected. The transition points on each bifurcation sequence and their corresponding parameter coordinates are connected or interpolated to form the boundary curve / interface between the "triggered phase and non-triggered phase," and the output is the bifurcation phase transition boundary data.

[0178] The bifurcation phase transition boundary data is divided into triggered and non-triggered regions in the parameter space. Within each triggered (or non-triggered) region, the most connected "stable kernel" region is identified, meaning that the perturbation parameter changes within this region do not cross the phase transition boundary and the triggering state remains consistent. The stable kernel is extracted through connected component aggregation (aggregating grid points / sampling points within the boundary according to adjacency) or density aggregation (finding density peaks in highly consistent regions and extending them to the boundary). After extraction, the geometric and statistical properties of the stable kernel are calculated as stability metrics, such as the stable kernel volume / area, minimum distance to the phase transition boundary (safety margin), boundary curvature (boundary steepness), or robust radius of the stable kernel center.

[0179] Stability measurement data is grouped by risk type, time window, or disturbance family, and robust statistics (such as quantiles, pruned means, Huber means, or medians) are calculated for each group to obtain a stability score. The stability score is then combined with the trigger determination corresponding to the calibration trigger surface. When the stability score is higher than a preset threshold or the stability kernel safety margin meets the conditions, an alert is output indicating whether an alert has been triggered and its confidence flag is displayed; otherwise, an alert downgrade (observation / manual review) or data completion request is output. The trigger conclusion, stability score, corresponding key disturbance families, and associated main contribution path indexes and mechanism segment information are encapsulated and output to form financial risk early warning result data.

[0180] In this embodiment, a bifurcation scenario parameter set is generated by performing normal perturbation bifurcation on the calibration trigger surface, and the phase transition boundary of the trigger state and the stable kernel are tracked on the propagation sensitivity tensor. Thus, the robustness of the early warning trigger is quantified by "the stable region and safety margin against perturbation". Then, the stability measure is robustly statistically aggregated to output the financial risk early warning result, so that the early warning is triggered only when the threshold and evidence perturbation remain consistent. This significantly suppresses false triggers caused by boundary noise and accidental evidence, reduces false alarms and improves the consistency and auditability of the early warning.

[0181] In an exemplary embodiment, graph structure fusion is performed on the subject identification data, subject relationship data, and event type data in the event evidence data to obtain risk evidence graph data, including steps 1002 to 1010. Wherein:

[0182] Step 1002: Perform multi-view identity folding on the subject identification data in the event evidence data to obtain the subject normalized identification data.

[0183] Step 1004: Perform relation tensor rewriting on the subject normalized identifier data and subject relation data to obtain relation skeleton tensor data.

[0184] Step 1006: Perform event phased arrangement on the event type data to obtain time phase event layer data.

[0185] Step 1008: Based on the relation skeleton tensor data and the time phase event layer data, perform layer alignment and fusion of evidence credibility injection on the subject normalized identifier data to obtain the initial risk evidence map data.

[0186] Step 1010: Load the initial risk evidence graph data into a graph database to obtain the risk evidence graph data.

[0187] Among them, multi-view identity folding is a process of cleaning, matching and aggregating multiple identifiers of the same subject under different data sources and different identifier systems, and generating a unified subject key.

[0188] Among them, the standardized identity data is a set of unified identity data after identity folding, which includes fields such as unified subject key, original identity mapping relationship, source information and matching confidence.

[0189] Among them, relation tensor quantization rewriting is a process of rewriting subject relations with unified subject keys and encoding multiple types of relations into multidimensional tensors according to "relation type × time × subject pair".

[0190] Among them, the relation skeleton tensor data is multidimensional sparse tensor data obtained by relation tensor quantization rewriting, which is used to represent the connection strength and direction of different relation types between subjects at different times.

[0191] Among them, event phasing orchestration is a process of classifying event types into phases such as triggering / spreading / decaying according to risk mechanisms and organizing them into a hierarchical event sequence in chronological order.

[0192] Among them, the time phase event layer data is the event layer structure data after phased arrangement, which includes event nodes, phase labels, timestamps, phase sequence constraint edges, and evidence reference information.

[0193] Among them, the layer alignment and fusion of evidence credibility injection is a fusion process in which the relationship layer and the event layer are aligned and connected according to a unified subject key and time constraint under the participation of evidence credibility weight, and conflicts are resolved.

[0194] The initial risk evidence graph data is a graph structure data generated by layer alignment and fusion, which includes main nodes, relationship edges, event nodes, time constraint edges, and their evidence weights and reference attributes.

[0195] Among them, graph database loading is the process of writing the initial risk evidence graph into the graph database according to the preset graph model and establishing indexes and consistency checks to form a queryable persistent graph.

[0196] Specifically, the entity identifier data is cleaned and standardized according to its source system, identifier type, and format (e.g., unified coding rules, noise removal, and verification bits). Cross-view mapping rules or mapping tables are then constructed (e.g., the correspondence between "securities code ↔ unified social credit code ↔ internal customer number ↔ account number ↔ product code"). Multiple identifiers of the same entity appearing in different views are aggregated. That is, deterministic matching (complete consistency, mapping table hit) and fault-tolerant matching (fuzzy matching, alias dictionary, address / legal person consistency) are used to generate a unified entity key. The mapping relationship between the original identifiers and the unified entity key is also output to form the normalized entity identifier data, which includes fields such as unified entity key, original identifier list, identifier source, matching confidence, and conflict flag.

[0197] The original subject identifiers in the subject relationship data are replaced with unified subject keys from the normalized subject identifier data to eliminate cross-source relationship breaks. Then, different types of subject relationships (such as equity / control, guarantee / pledge, counterparty, fund transfer, supply chain, and interbank exposure) are encoded into multi-type directed edges, and the key attributes of the edges (occurrence time, maturity time, amount / value, proportion, frequency, direction, risk weight, and evidence citation identifier) ​​are extracted as structured fields. A multi-dimensional relationship tensor is constructed using "relationship type × time × subject pair (source subject key, target subject key)" as the index. The edge weights (a function of amount / proportion / risk weight) are written into the corresponding tensor cells, and sparse indexes are retained to support efficient graph operations, resulting in relationship skeleton tensor data.

[0198] Event type data is mapped to phase labels according to risk mechanisms (e.g., trigger phase: abnormal indicators / negative announcements / rating downgrades; diffusion phase: cascading defaults / financing tightening / margin increases; decay phase: risk mitigation / successful refinancing / regulatory measures implemented). Timestamps, duration intervals, and event intensity are extracted from each event record in the event evidence data. Events are hierarchically arranged by phase, using time windows as units. Within a phase, events are sorted by time to form an event sequence, and temporal constraint edges are established between phases (e.g., "trigger phase → diffusion phase → decay phase") as well as temporal adjacency edges within the same phase. Each event node, along with its phase label, timestamp, intensity, evidence citation identifier, and subject attachment key, is packaged into time-phased event layer data, giving the event layer inherent time accessibility and phase semantics.

[0199] The system generates subject-layer nodes using the unified subject key of the subject-normalized identifier data as the alignment anchor, and decodes the relation skeleton tensor data into a set of relation-layer edges (different relation types correspond to different edge labels and weights). Furthermore, event nodes in the time-phase event layer data are connected to their corresponding subject nodes according to their subject attachment keys, forming "subject-event" edges. Inter-phase sequence constraint edges and inter-event causal candidate edges are added to the event layer. Simultaneously, evidence credibility weights are injected into all nodes and edges: evidence source credibility, cross-source consistency, time freshness, and confidence are extracted from the event evidence data, folded into node weights / edge weights or edge credibility attributes, and used for conflict resolution during alignment and fusion (e.g., selecting the higher credibility version or retaining and labeling conflicts when multi-source relations are inconsistent). Finally, the initial risk evidence graph data is obtained, containing subject nodes, relation edges, event nodes, time constraint edges, and their evidence weights and citation information.

[0200] Following a pre-defined graph data model, subject nodes, event nodes, and various edges are mapped to vertices and edge types in a graph database (including attribute fields and indexes). Indexes are created for the unified subject key, event type, timestamp, evidence citation identifier, edge type, and weight fields to support rapid subgraph retrieval by subject / time / risk type. Then, batch writing and consistency checks (such as deduplication, foreign key verification, and time edge closure checks) are performed. The original evidence text or evidence summary is then attached to the corresponding nodes / edges via attributes or external storage references. Finally, the loading process is completed, forming a queryable, incrementally updatable, and auditable risk evidence graph data.

[0201] In this embodiment, unified standardized entity identifier data is obtained by multi-view identity folding of entity identifiers, eliminating relationship breaks caused by cross-source entity inconsistencies. Then, the entity relationship tensor is tensorized and rewritten to form computable relationship skeleton tensor data, and the event types are phased and arranged to obtain time phase event layer data, aligning relationship propagation and event evolution within the same temporal semantic framework. Subsequently, layer alignment and fusion are completed under evidence credibility injection to generate initial risk evidence graph data, so that nodes and edges simultaneously carry auditable weights such as source credibility, cross-source consistency, and time freshness, thereby improving the accuracy and noise resistance of graph structure fusion. Finally, risk evidence graph data is loaded through a graph database to achieve a unified risk evidence base that is queryable, traceable, and incrementally maintainable, thereby improving the efficiency and reliability of subsequent target subgraph retrieval and mechanism chain reasoning and reducing false positives and false negatives.

[0202] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 2 As shown. This computer device includes a processor, memory, input / output interfaces (I / O), and communication interfaces. Those skilled in the art will understand that... Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0203] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0204] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0205] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the steps in the above method embodiments.

[0206] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0207] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A reasoning method for a large language model for financial risk early warning, characterized in that, The method includes: The structured financial data and unstructured text data corresponding to the financial risk warning entities are processed into event evidence data. The subject identification data, subject relationship data, and event type data in the event evidence data are fused using a graph structure to obtain risk evidence graph data; Large language model mechanism chain analysis is performed on the target subgraph data in the risk evidence graph data to obtain causal mechanism chain data; The causal mechanism chain data is compiled with assertions to obtain assertion test result data; Based on the assertion test results, the risk evidence graph data and the causal mechanism chain data are calibrated against false alarm constraints to obtain financial risk early warning results.

2. The method according to claim 1, characterized in that, The step of performing large language model mechanism chain analysis on the target subgraph data in the risk evidence graph data to obtain causal mechanism chain data includes: The target subgraph data is subjected to reverse mechanism skeleton extraction to obtain mechanism skeleton data; Virtual intervention and perturbation are performed on key node data or key edge data in the mechanism skeleton data to obtain mechanism fingerprint data; Based on the mechanism skeleton data and the mechanism fingerprint data, constrained large language model mechanism chain analysis is performed on the target subgraph data to obtain the causal mechanism chain data.

3. The method according to claim 2, characterized in that, The step of performing constrained large language model mechanism chain analysis on the target subgraph data based on the mechanism skeleton data and the mechanism fingerprint data to obtain the causal mechanism chain data includes: The mechanism skeleton data and the mechanism fingerprint data are constrained and compiled to obtain mechanism chain constraint data; Counterexample-driven disabled fragment mining is performed on the target subgraph data to obtain disabled mechanism fragment data; Based on the mechanism chain constraint data and the disabled mechanism fragment data, constrained large language model mechanism chain decoding analysis is performed on the target subgraph data to obtain candidate causal mechanism chain data; The candidate causal mechanism chain data is constrained and pruned to obtain the causal mechanism chain data.

4. The method according to claim 3, characterized in that, The step involves performing constrained large language model mechanism chain decoding analysis on the target subgraph data based on the mechanism chain constraint data and the disabled mechanism fragment data to obtain candidate causal mechanism chain data, including: The mechanism chain constraint data and the disabled mechanism fragment data are subjected to decoding control structuring processing to obtain decoding control data; Prefix counterexample detection is performed on the target subgraph data to obtain prefix disabled mapping data; Adversarial evidence projection is obtained by performing mechanism fingerprint inversion on the evidence path data in the target subgraph data to obtain evidence projection cost field data; Based on the decoding control data, the prefix disabling mapping data, and the evidence projection cost field data, the target subgraph data is subjected to reversible backtracking decoding with potential field constraints to obtain the initial candidate mechanism chain data. The initial candidate mechanism chain data is subjected to candidate solidification processing carrying constraint trajectories to obtain the candidate causal mechanism chain data.

5. The method according to claim 3, characterized in that, The counterexample-driven disallowed fragment mining of the target subgraph data yields disallowed mechanism fragment data, including: The target subgraph data and the mechanism chain constraint data are subjected to conflict explicit processing to obtain conflict candidate set data. Minimum cut counterexample extraction is performed on the conflict candidate set data to obtain counterexample seed fragment data; Based on the counterexample seed fragment data, the target subgraph data is subjected to adversarial expansion closure processing to obtain counterexample closure fragment data; The disabling mechanism fragment data is obtained by performing disabling normalization encoding on the counterexample closure fragment data.

6. The method according to claim 2, characterized in that, The reverse mechanism skeleton extraction of the target subgraph data to obtain mechanism skeleton data includes: The risk outcome endpoint node data in the target subgraph data is subjected to time-reverse causal candidate sowing to obtain backtracking seed set data; The backtracking seed set data is weighted by evidence credibility folding to obtain weighted backtracking frontier data; The weighted backflip front data is subjected to multi-objective minimum interpretation reduction processing to obtain skeleton candidate subgraph data; The skeleton candidate subgraph data is pruned to obtain the mechanism skeleton data.

7. The method according to claim 1, characterized in that, The step of performing false alarm constraint calibration on the risk evidence graph data and the causal mechanism chain data based on the assertion test results data to obtain financial risk early warning result data includes: The assertion test results data are subjected to assertion falsifiability spectrum decomposition to obtain assertion reliability spectrum data; Based on the assertion reliability spectrum data, risk propagation tensor mapping is performed on the risk evidence graph data and the causal mechanism chain data to obtain propagation sensitivity tensor data. A threshold inversion analysis driven by the false alarm upper limit is performed on the propagation sensitivity tensor data to obtain calibration trigger surface data; Based on the calibration trigger surface data, a stability bifurcation decision is made on the propagation sensitivity tensor data to obtain the financial risk early warning result data.

8. The method according to claim 7, characterized in that, The step of performing risk propagation tensor mapping on the risk evidence graph data and the causal mechanism chain data based on the assertion reliability spectrum data to obtain propagation sensitivity tensor data includes: The mechanism segment data in the causal mechanism chain data is used to perform slotted path anchoring on the risk evidence graph data to obtain mechanism anchoring path seed data. The propagation reachability expansion of the anchor path seed data of the aforementioned mechanism is performed by injecting spectral weights to obtain candidate propagation path set data; Adversarial consistency resampling is performed on the candidate propagation path set data to obtain robust propagation path subset data; Tensor-encode the robust propagation path subset data to obtain initial sensitivity tensor data; The initial sensitivity tensor data is subjected to low-rank decomposition and compression to obtain the propagation sensitivity tensor data.

9. The method according to claim 7, characterized in that, The step of performing stability bifurcation decisions on the propagation sensitivity tensor data based on the calibration trigger surface data to obtain financial risk early warning result data includes: The calibration trigger surface data is subjected to normal perturbation bifurcation processing to obtain bifurcation scenario parameter set data; Based on the bifurcation scenario parameter set data, phase transition boundary tracking is performed on the propagation sensitivity tensor data to obtain bifurcation phase transition boundary data; Stability kernels are extracted from the bifurcation phase transition boundary data to obtain stability metric data; Robust statistical aggregation of the stability measurement data yields the financial risk early warning results data.

10. The method according to claim 1, characterized in that, The process of fusing the subject identification data, subject relationship data, and event type data in the event evidence data into a graph structure to obtain risk evidence graph data includes: Multi-view identity folding is performed on the subject identification data in the event evidence data to obtain normalized subject identification data; The subject normalized identifier data and the subject relation data are rewritten using relation tensor quantization to obtain relation skeleton tensor data; The event type data is arranged in an event phased manner to obtain time phase event layer data; Based on the relational skeleton tensor data and the temporal phase event layer data, the subject normalized identifier data is subjected to evidence credibility injection layer alignment and fusion to obtain the initial risk evidence map data. The initial risk evidence graph data is loaded into a graph database to obtain the risk evidence graph data.

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