CN120833211A

CN120833211A - A digital value-added service method for debt demand analysis based on AI

Info

Publication number: CN120833211A
Application number: CN202510992492.5A
Authority: CN
Inventors: 唐华; 周清泉
Original assignee: Wupo Digital Technology Hangzhou Group Co ltd
Current assignee: Wupo Digital Technology Hangzhou Group Co ltd
Priority date: 2025-07-18
Filing date: 2025-07-18
Publication date: 2025-10-24

Abstract

The present invention discloses a digital value-added service method for AI-based debt demand analysis, which includes: receiving a set of original debt documents uploaded by a customer, generating a structured clause element set containing clause type, core elements, and expression characteristics; generating a risk quantification label for each key clause based on the structured clause element set; inputting the structured clause element set and its risk quantification label into a pre-trained clause value-risk collaborative optimization model to generate a set of optimized clause revision suggestions that maximize the user-set value target under the premise of controllable risks; generating a feasible revision strategy sequence sorted by fitness for the optimized clause revision suggestion set; and determining the final clause revision plan and supporting risk mitigation measures based on the feasible revision strategy sequence as the output of the digital value-added service. Utilizing the embodiments of the present invention, the accuracy and decision-making efficiency of debt management can be improved.

Description

Digital value-added service method for analysis of credited and debt demands based on AI

Technical Field

The invention belongs to the technical field of AI, in particular to a digital value-added service method for analyzing the demands of credited debt based on AI.

Background

With the complexity of the liability and liability relationship, the traditional manual auditing mode has obvious defects in efficiency, accuracy and risk prejudgement. Existing digitizing tools focus on text recognition or static risk assessment, and lack the ability to deep deconstruct clause semantics, dynamic risk quantification, and value-risk collaborative optimization. Especially in cross-industry, multi-scenario applications, the potential risk of terms is strongly related to market environment, counter-party credit, etc., whereas the prior art has difficulty in achieving real-time data-driven dynamic analysis. Moreover, contract revision suggestions often rely on empirical rules, lack of AI-based interactive deductions and personalized adaptations, resulting in insufficient solution executability.

Disclosure of Invention

The invention aims to provide a digital value added service method for the demand analysis of the credited debt based on AI, which solves the defects in the prior art and can improve the accuracy and decision efficiency of credited debt management.

One embodiment of the present application provides a digital value added service method for AI-based credited liability requirement analysis, the method comprising:

Receiving an original file set of the creditor and debt uploaded by a client, and carrying out semantic deconstructing and element extraction on key terms in the file through a deep semantic analyzer based on countermeasure training to generate a structured term element set containing term types, core elements and expression mode characteristics;

Based on the structured clause element set, calling a dynamically associated industry risk event library, and performing clause potential risk probability calculation and risk influence degree evaluation processing according to similarity matching of element features and risk event cases to generate a risk quantification label of each key clause;

Inputting the structured clause element set and the risk quantification label thereof into a pre-trained clause value-risk collaborative optimization model, performing multi-round iterative optimization simulation processing according to the current clause element state and a preset target, and generating a group of optimized clause revision suggestion sets which maximize a user set value target on the premise of controllable risk, wherein the collaborative optimization model trains the acceptance degree of the modification of the clause and the final execution result of the opponent under different negotiation strategies by simulating the reinforcement learning framework;

Performing personalized fitness scoring processing on the optimization clause revision suggestion set by combining the real-time acquired counter-party public credit image and market benchmark data through a lightweight dynamic matching engine to generate a feasible revision strategy sequence ordered according to fitness;

Based on the feasible revision strategy sequence, interactive counterfactual deduction is carried out, a liability performance probability change curve and expected damage distribution under a preset external situation after different revision strategies are adopted are visually displayed for a client, and a final clause revision scheme and a matched risk slow-release measure suggestion are determined to be used as output of the digital value-added service according to feedback selection of a deduction result by a user.

Optionally, the receiving the original document set of the credited debt uploaded by the client performs semantic deconstructing and element extraction on the key terms in the document by using a deep semantic analyzer based on the countermeasure training, and generates a structured term element set including the term type, the core element and the expression mode feature, including:

Multi-modal antagonism feature extraction is carried out on the original file set of the credited debt uploaded by the client, and a antagonism enhancement feature vector set integrating text semantics and document layout is obtained;

According to the antagonism enhancement feature vector set, performing clause boundary detection through a sequence labeling model driven by a bidirectional attention mechanism to obtain a preliminary clause segmentation map;

Performing element relation modeling on the preliminary clause segmentation map by using a graph convolution network to generate a clause element relation graph;

and (3) performing element deconstructment on the clause element relation graph through a semantic analyzer optimized by countermeasure training to generate a structured clause element set.

Optionally, based on the structured term element set, invoking a dynamically associated industry risk event library, performing term potential risk probability calculation and risk influence degree evaluation processing according to similarity matching of element features and risk event cases, and generating a risk quantification label of each key term, including:

According to the structured clause element set, carrying out heterogeneous graph embedded representation learning to obtain a multidimensional vector representation of the clause element;

invoking a dynamic risk event library, and performing event similarity graph matching on the multidimensional vector representation of the clause elements to obtain a similar risk event case set;

based on the similar risk event case set, carrying out Bayes-Monte Carlo risk probability modeling to obtain a potential risk probability value;

and carrying out multidimensional risk influence fusion evaluation according to the potential risk probability value and the event case influence data, and generating a risk quantification label of each key term.

Optionally, the inputting the structured clause element set and the risk quantification label thereof into a pre-trained clause value-risk collaborative optimization model, performing multiple rounds of iterative optimization simulation processing according to the current clause element state and a preset target, and generating a set of optimized clause revised suggestion sets that maximize a user set value target on the premise of controllable risk, where the collaborative optimization model trains the acceptance degree and final execution result of the clause modification by simulating opponents under different negotiating strategies based on a reinforcement learning framework, and includes:

Initializing a multi-target reinforcement learning state space according to the structured clause element set and the risk quantization label thereof to obtain an initial strategy space;

generating an antagonism network simulation antagonism negotiation process by using the double agents according to the initial strategy space to obtain a predicted value of the acceptance of the opponent;

According to the predicted value of the acceptance of the opponent, performing value-risk pareto front optimization to obtain an alternative repair scheme prototype set;

performing risk threshold constraint filtering on the prototype set of the alternative repairing scheme, and screening out a preliminary optimization suggestion set;

And generating an optimization clause revising suggestion set through multiple rounds of strategy gradient reinforcement learning iteration according to the preliminary optimization suggestion set.

Optionally, the step of performing personalized fitness scoring processing on the optimization clause revision suggestion set by combining the real-time acquired counter-party public credit image and market benchmark data through a lightweight dynamic matching engine to generate a feasible revision strategy sequence ordered according to the fitness comprises the following steps:

performing distributed feature vectorization on the optimization clause revised suggestion set to obtain a revised suggestion feature matrix;

according to the revised suggested feature matrix and the real-time counter-party credit portrait, carrying out dynamic weighted vector similarity calculation to obtain credit adaptation degree scores;

integrating the credit fitness score and market reference data, running a context awareness weight optimization algorithm, and calculating to obtain a weighted comprehensive fitness score;

Performing strategy priority ranking according to the weighted comprehensive suitability score to obtain a preliminary ranking sequence;

And (3) carrying out real-time feedback optimization on the preliminary sequencing sequence by using a lightweight dynamic matching engine, and outputting a feasible revision strategy sequence.

Optionally, based on the feasible revision policy sequence, performing interactive counterfactual deduction, visually displaying a liability performance probability change curve and an expected damage distribution under a preset external situation after adopting different revision policies to a client, and determining a final clause revision scheme and a matched risk slow-release measure suggestion as output of a digital value-added service according to feedback selection of a deduction result by a user, wherein the method comprises the following steps:

implementing multi-scenario counter-facts generation for the feasible revision policy sequence, and creating a deduction scenario set;

Based on the deduction scene set, performing liability performance probability Monte Carlo simulation to obtain probability change curve data;

Carrying out expected profit-loss dynamic modeling according to the probability change curve data and the market reference data to obtain a profit-loss distribution diagram;

and according to the damage distribution diagram and real-time feedback of the user, performing interactive decision optimization, and determining a final clause revision scheme and a matched risk slow-release measure suggestion.

Yet another embodiment of the present application provides a digital value added service system for AI-based credited liability requirement analysis, the system comprising:

The receiving module is used for receiving the original file set of the credited debt uploaded by the client, carrying out semantic deconstructing and element extraction on key terms in the file through a deep semantic analyzer based on countermeasure training, and generating a structured term element set containing term types, core elements and expression mode features;

the evaluation module is used for calling a dynamically associated industry risk event library based on the structured clause element set, performing clause potential risk probability calculation and risk influence degree evaluation processing according to similarity matching of element features and risk event cases, and generating a risk quantification label of each key clause;

The optimization module is used for inputting the structured clause element set and the risk quantification label thereof into a pre-trained clause value-risk collaborative optimization model, carrying out multi-round iterative optimization simulation processing according to the current clause element state and a preset target, and generating a group of optimization clause revision suggestion sets which maximize a user set value target on the premise of controllable risk, wherein the collaborative optimization model is used for training the acceptance degree of the modification of clauses and the final execution result of opponents under different negotiation strategies by simulating based on a reinforcement learning framework;

The matching module is used for carrying out personalized fitness scoring processing on the optimized clause revision suggestion set by combining the real-time acquired counter-party public credit image and market reference data through a lightweight dynamic matching engine to generate a feasible revision strategy sequence ordered according to the fitness;

The determining module is used for carrying out interactive counterfactual deduction based on the feasible revision strategy sequence, visually displaying a debt performance probability change curve and expected damage distribution under a preset external situation after different revision strategies are adopted for a client, and determining a final clause revision scheme and a matched risk slow-release measure suggestion as output of the digital value-added service according to feedback selection of a deduction result by a user.

A further embodiment of the application provides a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of the preceding claims when run.

Yet another embodiment of the application provides an electronic device comprising a memory having a computer program stored therein and a processor configured to run the computer program to perform the method recited in any of the preceding claims.

Compared with the prior art, the digital value added service method based on the analysis of the right and debt demands of the AI receives an original document set of the right and debt uploaded by a client, generates a structured clause element set containing clause types, core elements and expression mode characteristics, generates a risk quantization label of each key clause based on the structured clause element set, inputs the structured clause element set and the risk quantization label thereof into a pre-trained clause value-risk collaborative optimization model, generates a set of optimized clause revision suggestion set for maximizing a user set value target under the premise of controllable risk, generates a feasible revision strategy sequence according to the adaptation degree, and determines a final clause revision scheme and matched risk slow-release measure suggestion based on the feasible revision strategy sequence to serve as output of the digital value added service, so that the accuracy and decision efficiency of the right and debt management can be improved.

Drawings

Fig. 1 is a hardware block diagram of a computer terminal of a digital value-added service method for analyzing the demand of liability and liability based on AI according to an embodiment of the present invention;

FIG. 2 is a flowchart of a digital value added service method for analyzing the demand of the credited debt based on AI according to the embodiment of the invention;

fig. 3 is a schematic structural diagram of a digital value-added service system for AI-based credited debt demand analysis according to an embodiment of the present invention.

Detailed Description

The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.

The embodiment of the invention firstly provides a digital value added service method for the analysis of the demand of the credited debt based on AI, which can be applied to electronic equipment such as a computer terminal, in particular to a common computer and the like.

The following describes the operation of the computer terminal in detail by taking it as an example. Fig. 1 is a hardware block diagram of a computer terminal of a digital value-added service method for analyzing the demand of liability and liability based on AI according to an embodiment of the present invention. As shown in fig. 1, the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

The non-volatile storage medium may store an operating system and a computer program. The computer program comprises program instructions that, when executed, cause the processor to perform any one of a number of digital value added service methods based on AI-based credited and debt demand analysis.

The processor is used to provide computing and control capabilities to support the operation of the entire computer device.

The internal memory provides an environment for the execution of a computer program in a non-volatile storage medium that, when executed by the processor, causes the processor to perform any one of a number of digitized value added service methods based on AI-based credited liability requirement analysis.

The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by those skilled in the art that the architecture shown in fig. 1 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements may be implemented, as a particular computer device may include more or less components than those shown, or may be combined with some components, or may have a different arrangement of components.

It should be appreciated that the Processor may be a central processing unit (Central Processing Unit, CPU), it may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

Referring to fig. 2, an embodiment of the present invention provides a digital value added service method for AI-based credited liability requirement analysis, which may include the steps of:

s201, receiving an original file set of credited debt uploaded by a client, and carrying out semantic deconstructing and element extraction on key terms in the file through a deep semantic analyzer based on countermeasure training to generate a structured term element set containing term types, core elements and expression mode features;

Specifically, multi-modal antagonism feature extraction can be implemented on the original file set of the creditor and debt uploaded by the client, so as to obtain a antagonism enhancement feature vector set integrating text semantics and document layout;

Multimodal data fusion processing

After the customer uploads the original document set (e.g., PDF contract, scanned image, electronic document), the system first starts the multimodal data fusion engine. The engine adopts a double-channel feature extraction architecture:

Text semantic channel-parsing text content in a file through a pre-trained language model (e.g., base, bidirectional Encoder Representations from Transformers's reference version), outputting a word vector sequence (Word Embedding Sequence, WES). Each word vector is a 768-dimensional floating point number array capturing the contextual semantics of the word.

Document layout channel-the visual structure of a file is analyzed using a convolutional neural network (e.g., a 50-layer version of ResNet-50,Residual Network). The input is a block region (a block divided into 224×224 pixels) of the document image, and the output block feature vector (Patch Feature Vector, PFV) contains physical layout information such as a table position, a stamp region, a paragraph indentation, and the like.

The outputs of the two channels are aligned by a Cross-modal Attention fusion module (Cross-Modal Attention Fusion Module, CMAFM), namely, text word vectors and corresponding image block features calculate Attention Weights (AW), and the Weight value range is 0-1 to reflect the graphic association strength. For example, if a text description of a "default term" is located at the right bottom of the contract with the stamp, the graphic attention weight may be up to 0.92, while the heading text weight of the header may be only 0.15.

Robust against training

To promote the adaptability of features to fuzzy scan, handwritten annotations, the system introduces the generation of an countermeasure training Framework (GENERATIVE ADVERSARIAL TRAINING Framework, GATF):

A Generator (Gen) receives the noisy original features (e.g., adding gaussian noise with standard deviation set to 0.1) and tries to reconstruct the fused feature vector. The generator structure is a 5-layer fully connected neural network (Fully Connected Neural Network, FCNN), and the number of neurons in each layer is 1024, 512, 256, 128 and 64 respectively.

A discriminator (Discriminator, dis) for discriminating whether the input features are from the original data or the reconstructed data is generated. The arbiter uses a 3-layer convolutional neural network (Convolutional Neural Network, CNN), with a convolutional kernel size of 3 x 3, and a step size (Stride) of 1.

Both are trained alternately by Minimum Maximum Game (MMG) where the generator goal is to minimize the recognition accuracy of the arbiter (goal drops to 50%) and the arbiter maximizes the accuracy (goal approaches 100%). After 200 rounds of training iterations (epochs), the system outputs a set of challenge-enhanced feature vectors (ADVERSARIAL ENHANCED Feature Vector Set, AEFVS), each with dimensions 1024, fused with a robust representation of semantics and layout.

Dynamic feature normalization and compression

For feature scale differences caused by different file sizes, the system performs dynamic range Normalization (DYNAMIC RANGE Normalization, DRN):

The Mean (Mean, μ) and standard deviation (Standard Deviation, σ) of all feature vectors are calculated, and a linear transformation (vector value- μ)/σ is performed on each vector to conform the data distribution to the standard normal distribution (Mean 0, standard deviation 1).

The dimension is reduced to 256 dimensions by principal component analysis (PRINCIPAL COMPONENT ANALYSIS, PCA), leaving 95% of the original information (determined by the eigenvalue cumulative contribution). And finally generating a unified-scale antagonism enhancement characteristic vector set for downstream processing.

Sequence annotation model construction

The system adopts a two-way long and short Term Memory network (Bidirectional Long Short-Term Memory, biLSTM) as an infrastructure, and the core of the system is to capture the context dependence through Forward LSTM (Forward LSTM) and Backward LSTM (Backward LSTM, B-LSTM):

The input layer receives a set of contrast enhancement feature vectors (256-dimensional vector sequence) with a Time Step (Time Step, TS) equal to the number of words in the file.

F-LSTM processes the sequence from left to right, hiding layer dimension 128, and B-LSTM processes from right to left, hiding layer dimension 128. The two outputs are stitched into 256-dimensional State Vectors (SVs) at each step.

Attention mechanism driven boundary recognition

The Self-attention mechanism (Self-Attention Mechanism, SAM) is superimposed at BiLSTM output layer:

For the state Vector of each time step, a Query Vector (QV), a Key Vector (Key Vector, KV), a Value Vector (Value Vector, VV) are calculated, and the dimension is 64.

The attention weight is calculated by scaling the dot product attention (Scaled Dot-Product Attention, SDPA) formula: weight = Softmax ((q·kt)/v64), where v64=8 is the scaling factor.

And outputting a weighted value vector sequence, and highlighting key position characteristics (such as attention weight of clause starting word "the contract of the Chinese character" can reach 0.85).

Conditional random field optimized tag sequences

Finally, generating a clause boundary tag through a conditional random field (Conditional Random Field, CRF) layer:

define tag sets B-Bound (clause start), I-Bound (clause inner), O (non-clause).

CRF learns inter-tag transfer rules (e.g., B-Bound must be followed by I-Bound or O, and B-Bound is prohibited from directly jumping to O).

A preliminary clause segmentation map (PRELIMINARY CLAUSE SEGMENTATION MAP, PCSM) is output, and a Start-stop position Index (Start Index, SI; end Index, EI) for each clause is stored in JSON format. For example, it is detected that "payment terms" are located in the 120-215 word interval.

map node and edge definitions

Converting the clause segmentation map to an iso-patterning structure (Heterogeneous Graph Structure, HGS):

Nodes (Node, N) are divided into three types, namely a clause main body (such as a party), action predicates (such as payment), and numerical constraints (such as a RMB 100 ten thousand yuan). Each node is initialized with a word vector.

Edge (E) defines a type according to the syntax dependency, including:

a Subject-Verb (SV), weight 1.0;

a move guest relationship (Verb-Object, VO), weight 0.9;

Attribute-Modification (AM), weight 0.7;

Logical-Connection (LC), weight 0.8.

Graph roll-up network feature propagation

Relationship modeling using a two-layer graph rolling network (Graph Convolutional Network, GCN):

The first layer GCN, input node feature dimension 256, output dimension 128. The aggregation function (Aggregation Function, AF) is a weighted average of the neighbor node features, the weights being determined by the edge type. For example, when the "a-party" node aggregates "payable" features by SV edges, the weight takes 1.0.

The second layer GCN, input dimension 128, output dimension 64. A gating mechanism (GATING MECHANISM, GM) is introduced that computes the information retention probability (range 0-1) using Sigmoid function, filtering noise connections (e.g., AM edges more than 5 nodes apart may be assigned a retention probability of 0.2).

Relationship graph generation and compression

Outputting a clause element relationship graph (Clause Element Relation Graph, CERG):

the node features are updated as 64-dimensional vectors, characterizing the semantics of the fused context.

Pruning sparse edges, removing edges with weights below the threshold of 0.3 (e.g., weakly correlated LC edges).

Stored as adjacency matrices (Adjacency Matrix, AM) and Feature Matrices (FM), the matrices being of size n×n and n×64 (N being the total number of nodes).

Semantic parser architecture design

The semantic parser (SEMANTIC PARSER, SP) is based on a Graph-to-Sequence Model (G2S):

encoder-input relationship graph (CERG), output node enhancement vector (Enhanced Node Vector, ENV), dimension 64, using two layers of GCN processing.

Decoder-the structured fields are generated step by step using unidirectional LSTM (Unidirectional LSTM, uni-LSTM). The hidden layer dimension 128, the initial state is the average of all node vectors.

Optimizing generalization ability for countermeasure training

Introduction of Domain countermeasure training (Domain ADVERSARIAL TRAINING, DAT) promotes adaptability across contract types:

Master task classifier, prediction element type (such as clause type, core element, etc.), 3-layer fully connected network, output dimension equal to label class number (e.g. 20 classes).

Challenge task classifier-attempt to determine from which domain of contracts (e.g., loan contracts, supply chain financial contracts) the input comes, structured with the master task classifier.

The direction of the gradient of the countermeasure task is reversed by a gradient reversing layer (GRADIENT REVERSAL LAYER, GRL) during training, the main task minimizes the classification error (target error < 5%), the countermeasure task maximizes the domain classification error (target error > 40%), and the encoder is forced to generate domain independent features.

Structured element set generation

The final output structured clause element set (Structured Clause ELEMENT SET, SCES) contains three substructures:

The term type is automatically categorized into predefined labels (e.g. "payment term", "default liability term") with a confidence score (Confidence Score, CS) in the range 0-1.

The core element is to extract key entities (such as the sum of ' USD 500,000 ', term ' 30 working days ') and associate roles (such as ' creditor ' debtor ').

The expression mode features are to record the statistical characteristics of legal expression style (such as ' should ' vs ' must), fuzzy word frequency (such as ' rational term ' frequency of occurrence), etc.

The data is stored in a structured JSON format, for example, the output of one "late gold clause" is:

{

"clause _type": "breach liability clause",

"core_elements": [

{ "Entity": "daily interest rate", "value": "0.05%", "role": "penalty criteria" },

{ "Entity": "date of day", "value": "date of expiration" and "role": "trigger condition" }

],

"expression_features": {

"Modal-verb": "response",

"ambiguity_count": 0

}

}。

The method first processes the credited liability document uploaded by the customer through a counter-training deep semantic parser that can identify and deconstruct key terms in the contract, converting them into structured data elements. Through countermeasure training, the analyzer has stronger generalization capability, can accurately identify contract clauses in different formats and expression modes, extract clause types, core right obligation factors and unique expression features, convert unstructured contract texts into standardized and computable structured data, and lay a foundation for subsequent risk analysis and clause optimization. The countermeasure training ensures the strong adaptability of the parser to various contract texts, and avoids the limitation of the traditional rule engine.

S202, calling a dynamically associated industry risk event library based on the structured clause element set, and performing clause potential risk probability calculation and risk influence degree evaluation processing according to similarity matching of element features and risk event cases to generate a risk quantification label of each key clause;

specifically, according to the structured clause element set, heterogeneous graph embedded representation learning can be performed to obtain a multidimensional vector representation of the clause element;

The system receives the structured clause element set (Structured Clause ELEMENT SET, SCES) generated in the previous step. The data set contains three types of core information including term type (e.g., payment condition, liability violations), core elements (e.g., interest rate value, liability rate), and presentation features (e.g., absolute time limit presentation in "pay-off within 30 days"). To capture complex relationships between elements, a heterogram (Heterogeneous Graph, HG) is constructed with each clause element as a Node, the Node attributes containing element types and feature values, logical associations between elements (e.g., dependencies of "interest" and "complex interest calculation"), semantic similarities (e.g., close relationships of "compensation" and "compensation") as edges (Edge). The graph neural network (Graph Neural Network, GNN) is adopted for embedded learning, each node generates 128-dimensional Base Vectors (BV) through a word embedding layer (such as BERT) during initialization, and neighbor node information is aggregated through a 3-layer graph rolling network (Graph Convolutional Network, GCN) to learn inter-node dependence. For example, a "breach Jin Tiaokuan" node may aggregate the characteristics of its associated neighbor nodes, such as "odds", "trigger conditions", and the like. A256-dimensional multi-dimensional vector representation (Multidimensional Vector Representation, MVR) is finally output, the dimensions 0-63 of which encode clause type semantics, 64-127 of which encode core element numerical features, and 128-255 of which encode expression implicit risk propensity.

In order to solve the information fusion problem brought by the diversity of edge types (such as logic associated edges and semantically similar edges) in the heterograms, a Meta-Path (Meta-Path) attention mechanism is adopted. Defining key element path modes, such as 'clause type → core element → expression mode' (path type PT 1), 'core element → similar element → history case' (path type PT 2). For each meta-path instance (e.g., a "interest term" is associated with a "5%" element by PT 1), an Attention Weight (AW) between nodes is calculated, the Weight being determined by both the learnable parameters and the node feature similarity. For example, the PT2 path of the "floating interest" node and the "LIBOR benchmark" node are highly semantically related, AW may be as high as 0.9, while the PT1 path of the "fixed mortgage" node is only as different as 0.2 due to type. A Path-Aware Vector (PAV) is generated by weighting and aggregating neighbor information under different meta-paths. And finally, splicing the basic vector BV with all PAVs, and outputting the MVR with local structurality and global semantics through full-connection-layer dimension reduction fusion. This process is performed distributed across the NVIDIA DGX server cluster, and a single A100 GPU can handle iso-patterning with 10,000 nodes.

To promote the robustness of the vector representation, an anti-regularization (ADVERSARIAL REGULARIZATION, AR) is introduced. During the training phase, the challenge samples are constructed by randomly adding noise edges to the input graph (e.g., incorrectly associating "pay-by-stage" nodes to "cross-border jurisdiction" clauses), or perturbing node features (e.g., replacing the 5% of the benefit value with a missing value). By minimizing the vector distance of clean samples from the challenge samples (using cosine similarity constraints), the model is forced to ignore extraneous disturbances and focus key features. After training is completed, the MVR of each clause element satisfies the characteristics that the similar elements (such as "unreliability" clauses in different contracts) are close in distance in vector space (Euclidean distance is less than 0.3), and the high risk elements (such as "no upper bound reimbursement") are significantly activated in a specific dimension (such as dimension 201) (scalar value is greater than 0.8). This MVR will be the underlying characterization of subsequent risk matching.

The dynamic risk event Library (DYNAMIC RISK EVENT Library, DREL) stores three types of data, historical judicial cases (e.g., a certain house enterprise triggered a chain liability crisis due to "cross violation terms"), industry regulatory penalties (e.g., fine by silver guard on "overlook terms"), market overt violations (e.g., loss of hundreds of millions by a certain company due to "exchange rate fluctuation terms"). Each event in the library is abstracted as an "event map" (EVENT GRAPH, EG) in which nodes are event elements (e.g., parties to events, clause content, loss amount) and edges are element causal relationships (e.g., "interest rate up → cash flow break → default"). DREL accesses external data sources (such as referee paperwork network, central credit system) in real time through API interface, and updates about 5,000 events daily. Upon receiving the MVR for the clause element, the matching engine performs graph similarity matching with the node vectors for all EG's in DREL (GRAPH SIMILARITY MATCHING, GSM).

GSM is divided into two phases:

And (3) node level matching, namely calculating the vector similarity between the MVR of the clause element to be analyzed and each node in the event diagram. The vector dimensions are treated as time series with a modified DTW algorithm (DYNAMIC TIME WARPING, DTW), allowing for local dimension misalignment. For example, the MVR for a "grace period clause" is 0.15 (considered a match within a threshold of 0.2) from the DTW for the "deferred trigger condition" node in the "A company debt deferred event".

And (3) matching the graph structure, namely further calculating the graph editing distance (GRAPH EDIT DISTANCE, GED) for the sub-graph with the successful node matching. And (3) adding/deleting edges to make the minimum operation cost of isomorphism between the clause diagram to be analyzed and the event subgraph be the GED value. For example, the GED of a certain "mortgage guaranty clause" graph and a "B bank quality deposit devaluation event" graph is 2 (1 "devaluation rate associated edge" needs to be added and 1 "irrelevant third edge" needs to be deleted).

The DTW distance (weight 0.6) and the GED (weight 0.4) are combined to generate a combined similarity score (Comprehensive Similarity Score, CSS). The events with CSS > 0.7 are filtered, a similar risk event case set (SIMILAR RISK EVENT CASE SET, SRECS) is generated in descending order of score, and a single query returns 12 cases on average.

To improve efficiency, a hierarchical index strategy is adopted:

first layer, hash buckets based on clause type (e.g. "vouch-for class" clauses only match vouch-for related events in DREL).

The second layer, using HNSW algorithm (HIERARCHICAL NAVIGABLE SMALL WORLD, HNSW) to construct neighbor index for event map central node vector, fast recall Top 200 candidate event.

And a third layer, performing accurate GSM calculation on the candidate events in parallel.

The system updates the index every hour, ensuring that the newly added events can be matched in real time. Case set SRECS contains key metadata such as event raw text, loss amount (units: ten thousand yuan), occurrence probability statistics (e.g., 15% of historical violations of a certain category of terms in the real estate industry).

For each case in SRECS, its risk triggering conditions (e.g. "trigger compensation terms when raw material price fluctuates > 30%) and outcome data (actual probability of violation 22%) are extracted. A Bayesian Network (BN) model is built in which Network nodes include term element variables (e.g. "price volatility threshold"), external environment variables (e.g. "bulk commodity index"), risk result variables (e.g. "liability violations"). The inter-node dependencies are built based on causal relationships in the case, such as "threshold set too high (parent node) →easy trigger compensation (child node) →cash flow pressure increase (grandchild node)". Conditional probability tables (Conditional Probability Table, CPT) are calculated using maximum likelihood estimates (Maximum Likelihood Estimation, MLE). For example, when "price fluctuation Threshold >25%", the conditional probability P (Trigger |threshold) of "Trigger compensation" is 0.65.

Based on bayesian networks, a monte carlo simulation (Monte Carlo Simulation, MCS) is performed:

Parameter sampling, namely setting probability distribution for uncertainty variables (such as price fluctuation of bulk commodity in the next 3 years). If the case has historical data, a kernel density Estimation (KERNEL DENSITY Estimation, KDE) is adopted to fit a distribution (such as Gamma distribution Gamma (alpha=2.1, beta=0.8)) and if the case has no data, a uniform distribution (such as 5% -50% of fluctuation) preset by an expert is adopted.

Propagation calculation 10,000 random samples are performed. At each sampling, a random state (such as "fluctuation=28%, threshold=25% →trigger compensation=yes") is generated according to the CPT and the variable distribution, and the occurrence frequency of the risk result (such as "default" state) is finally counted.

Probability output the frequency of occurrence of the risk event is calculated as a potential risk probability value (Potential Risk Probability Value, PRPV). For example, if a "exchange rate hook term" is found to be 2,150 loss overruns in 10,000 simulations, PRPV =21.5%.

To improve PRPV reliability, sensitivity analysis (SENSITIVITY ANALYSIS, SA) was introduced:

Key parameter disturbance, namely taking a value of + -10% of a high-influence node (such as 'industry scenic index') in the Bayesian network, and rerun the MCS.

Scene comparison PRPV differences under optimistic scenes (e.g. GDP increased by 7%), pessimistic scenes (e.g. GDP increased by 3%) are calculated.

Finally, probability values with confidence intervals, such as "basic probability 21.5%,95% confidence interval [19.2%, 23.8% ], are output. All computations are performed distributed on a cloud computing platform (e.g., AWS Batch), and single-pass simulation takes less than 8 seconds.

Risk impact assessment covers four dimensions:

financial impact (FINANCIAL IMPACT, FI) linear scaling is performed in combination with the current contract size based on the actual loss data in the case (units: ten thousand yuan). For example, in a case where "reimbursement terms" result in 500 ten thousand yuan, the current contract amount is 10 times that, the benchmark fi=5,000 ten thousand yuan.

Legal influence (LEGAL IMPACT, LI) is based on the judicial case outcome ranking assignment. Such as "clause invalid" assigning a score of 10 (highest risk) and "partial support" assigning a score of 5.

Reputation influencing (Reputational Impact, RI) is calculated by a public opinion analysis model. For example, if a certain "bundled sales term" caused a media negative report amount to increase by 200%, ri=8 points.

Conduction influence (Contagion Impact, CI) to assess risk chain reactions. If a certain "cross violation term" results in 3 associated enterprises breaking, ci=9 points.

Multi-source data fusion was performed using evidence Theory (Dempster-Shafer Theory, DST):

The evidence of the impact of each dimension is converted into a basic probability distribution (Basic Probability Assignment, BPA). For example, financial impact fi=5,000 ten thousand yuan corresponds to BPA: { low risk: 0.2, medium risk: 0.6, high risk: 0.2}.

BPA of different dimensions are merged by synthesis rules (Dempster's Rule). For example, a joint probability distribution is generated after synthesis of high risk BPA in the financial dimension (0.2) with medium risk BPA in the legal dimension (0.7).

A confidence Function (belef Function, bel) and a plausibility Function (Plausibility Function, pl) are calculated to determine the risk level confidence interval.

Finally, a risk quantification tag (Risk Quantification Label, RQL) is generated, containing the following fields:

Risk probability PRPV values (e.g., 21.5%);

a composite risk index (Composite Risk Index, CRI) ranging from 0 to 100, mapped from the fused DST results (e.g., cri=73);

Major impact dimension, ordered by impact value (e.g., financial > legal > reputation);

Sensitivity summary: key sensitivity parameters and their influence amplitude (e.g. "every 1% decrease in GDP, probability +2.3%");

The label is visualized by color coding CRI <30 green (low risk), 30-70 yellow (risk of stroke) >70 red (high risk). The tag will be the core input for the next phase clause optimization.

The system carries out intelligent matching on the structural clauses and the dynamically updated industry risk event library, identifies the risk modes in the historical cases through a similarity algorithm, and quantifies the risk occurrence probability and the potential influence degree of the current clauses by using a probability statistical method. The risk event library continuously brings up the latest judicial case, supervision punishment and market default events, achieves objective quantification of contract risks and prospective early warning, and helps users identify legal risks and business risks hidden in terms. The dynamically updated risk library ensures that the assessment results reflect the latest market environment and judicial practices.

S203, inputting the structured clause element set and the risk quantification label thereof into a pre-trained clause value-risk collaborative optimization model, performing multi-round iterative optimization simulation processing according to the current clause element state and a preset target, and generating a group of optimized clause revision suggestion sets for maximizing a user set value target on the premise of controllable risk, wherein the collaborative optimization model trains the acceptance degree of the modification of the clause and the final execution result of the opponent under different negotiation strategies by simulating the reinforcement learning frame;

Specifically, a multi-objective reinforcement learning state space can be initialized according to the structured clause element set and the risk quantization label thereof, and an initial strategy space is obtained;

The system receives a structured clause element set (Structured Clause ELEMENT SET, SCES) generated by the front-end process and a risk quantification tag (Risk Quantification Label, RQL) corresponding to each clause. SCES contains three types of core data, 1) clause type (such as payment period, default responsibility and guarantee mode), 2) core elements (such as monetary value, time node and trigger condition), and 3) expression mode characteristics (such as fuzzy vocabulary density and passive language duty ratio). The RQL then contains two dimensions, a potential risk probability value (Potential Risk Probability, PRP, range 0-1) and a risk impact Coefficient (RISK IMPACT Coefficient, RIC, level 1-5). The initialization process first maps SCES and RQL into machine-understandable State Vectors (SVs). Specifically, each clause type is encoded as a One-hot Vector (OHV), e.g., payment cycle type is encoded as [1, 0. ], numerical parameters (e.g., annual rate 8%) in the core element are directly normalized (Normalization, NOR, e.g., mapped to the [0,1] interval), and text expression features are converted to 128-dimensional dense vectors by a word embedding layer (Word Embedding Layer, WEL). PRP and RIC of RQL are then stitched into a 2-dimensional Risk Vector (Risk Vector, RV). Finally, a contract containing N key terms, whose state vectors SV are spliced from the vectors of all terms in order, forms the starting point of a high-dimensional state space (STATE SPACE, SS).

The preset targets (Preset Objectives, PO) are set by the user at service start-up and generally comprise three categories, 1) a value target (Value Objectives, VO, for example, shortening the payback period by 20% and reducing the financing cost by 15%), 2) a risk tolerance target (Risk Tolerance Objectives, RTO, for example, controlling the overall contract risk probability to be below 10%), and 3) constraint conditions (Constraints, CONS, for example, mortgages must not be added and negotiation rounds are less than or equal to 3 times). The system quantifies these targets into components of a Multi-target bonus function (Multi-objective Reward Function, MORF). For example, the bonus function corresponding to the reduced payback period may be designed to be the actual reduced number of days/the target reduced number of days multiplied by the weighting factor WV (Value Weight). Meanwhile, the penalty function of risk overrun is (actual risk probability-risk tolerance threshold) x penalty coefficient WP (Penalty Weight). The key to the initialization phase is to combine the state vector SV with a preset target PO, defining an Action Space (AS). The action space is made up of all feasible clause revision operations, such as 1) modifying the payment period (action code A01), 2) adjusting the rate of the deposit violation (A02), and 3) adding the bond deposit clause (A03). Each action is accompanied by a modification parameter (e.g., change payment period from 90 days to 60 days). The final initial policy space (Initial Policy Space, IPS) is the cartesian product of the state space SS and the action space AS, providing a search range for the subsequent optimization simulation.

To improve optimization efficiency, the system employs a hierarchical state Representation (HIERARCHICAL STATE presentation, HSR). The first layer is a Global State (GS) of the contract, which contains aggregate indexes such as overall risk probability mean, weighted value score and the like, the second layer is a clause cluster State (Clause Cluster State, CCS), the clauses are grouped according to functional relevance (for example, all payment related clauses are clustered), and the third layer is a single clause State (Single Clause State, SCS). The structure enables the reinforcement learning agent to grasp macroscopic contract situation and focus on local term optimization. After the initial policy space IPS is built, the system runs a round of random policy sampling (Random Policy Sampling, RPS), generating 500-1000 sets of random revisions as an initial population (Initial Population, IPOP) for starting the subsequent optimization process. All state vectors and motion codes compress dimensions through Feature Hashing (FH), so that the computing efficiency is ensured.

The Dual Agent generation challenge Network (DA-GAN) is a core engine for simulating negotiations, and consists of two deep neural Network agents, 1) a my Agent (Proposer Agent, PA) responsible for generating a clause revision, and 2) an Opponent Agent (OA) responsible for evaluating and feeding back acceptance probabilities. The PA adopts a condition generator (Conditional Generator, CG) structure, inputs the current state vector SV and the my target PO, and outputs the current state vector SV and the my target PO as a revised action sequence (such as [ A01:60 days, A02: +5% ]). OA uses a convolutional-attention hybrid encoder (Convolutional-Attention Hybrid Encoder, CAHE) to input revisions generated for PA, a database of counter-party historic contracts, and real-time acquired counter-party credit figures (Opponent Credit Profile, OCP, including credit rating, industry status, recent litigation records, etc.). OCP is dynamically updated by Public data crawler (Public DATA CRAWLER, PDC) to ensure simulated authenticity.

The challenge simulation process is iterative in that after the PA generates the revision, the OA predicts opponent acceptance (Opponent Acceptance Probability, OAP) based on three dimensions, 1) economic feasibility (Economic Feasibility, EF) calculates the impact of the revision on the opponent's cash flow (e.g., pays ahead to have its funds turnover drop value), 2) risk matching (Risk Compatibility, RC) evaluates the opponent's historical tolerance for risk of that term (e.g., industry enterprises accept on average the terms of RIC > 3), 3) policy consistency (STRATEGIC CONSISTENCY, SC) analyzes the propensity of the opponent to modify similar terms in recent contract contracts (e.g., accept the probability of payment cycle shortening in the past 6 negotiations). The output layer of the OA uses a Sigmoid activation function (Sigmoid Activation Function, SAF) to convert the composite score to an acceptability prediction value OAP between 0-1. For example, when the PA proposes "payment period reduced from 90 days to 60 days", the OA may calculate oap=0.65 (representing 65% probability of acceptance) in conjunction with the counter-party cash flow model.

To enhance simulated reality, DA-GAN introduces a dynamic challenge training (DYNAMIC ADVERSARIAL TRAINING, DAT) mechanism. After each simulation run, the OA evaluates the authenticity of the PA-generated solution based on the authenticity history negotiation data (stored in the case knowledge base (Case Knowledge Base, CKB)). If the PA-generated scheme is determined by OA to be false (e.g., to put forward terms that industry is unlikely to accept), the Policy Gradient (PG) of the PA will be penalized. Meanwhile, the prediction accuracy of the OA is continuously optimized through historical case backtracking verification (Historical Case Retrospective Validation, HCRV), namely the acceptance prediction value OAP_predicted of the OA on the historical case is compared with an actual signing result OAP_actual, and the OA network weight is updated by adopting a Cross entropy loss function (Cross-Entropy Loss Function, CELF). After tens of thousands of countermeasure training, the DA-GAN can accurately simulate the negotiation behavior patterns of opponents in different industries and different credit levels.

The opponent acceptance prediction Value (OAP) output by the DA-GAN and the Value target completion (Value ACHIEVEMENT DEGREE, VAD) and the risk control level (Risk Control Level, RCL) are combined to construct a Three-dimensional optimization target space (Three-dimensional Optimization Objective Space, tous):

X-axis (value dimension) vad= Σ (clause value improvement amount X value weight WV);

Y-axis (risk dimension): rcl=1- (current contract risk probability/risk tolerance threshold);

z-axis (acceptance dimension) OAP is directly employed.

The system searches for the pareto optimal solution set (Pareto Optimal Solution Set, POSS) in this space using Non-dominant ordering genetic algorithm II (Non-dominated Sorting Genetic Algorithm II, NSGA-II). The initial population samples the IPOP from a previous random strategy, with each individual representing a complete revision scheme (e.g., a sequence of 5 clause modification actions).

The core operations of NSGA-II include:

1) Fast Non-dominant ranking (FNS) the individuals were classified into different Front Ranking (FR) according to their performance on three targets in TOOS. For example, a solution is classified as Rank1 front if it is superior to other 90% solutions in all of value, risk, and acceptance.

2) And (5) calculating the crowding degree (Crowding Distance Calculation, CDC) which is used for calculating the distribution density of the individual in the target space in the same front edge grade. The individuals with large crowding degree (if the similar schemes are not arranged around the scheme A, the crowding degree is high) are reserved preferentially, and the diversity of solution collection is ensured.

3) Elite retention strategy (ELITISM STRATEGY, ES) is that the first 20% of optimal individuals are retained in each generation of evolution and directly enter the next generation, so that high-quality solution loss is avoided.

4) The simulated binary crossover (Simulated Binary Crossover, SBX) swaps the clause modification actions of the two parent schemes with a probability pc=0.85 (Crossover Probability). For example, the "payment cycle shortening" action of parent 1 is exchanged with the "default gold reduction" action of parent 2.

5) Polynomial variation (Polynomial Mutation, PM) by randomly adjusting the action parameters (e.g., varying the payment period from 60 days to 45 days or 75 days) with a probability pm=0.15 (Mutation Probability).

After 100-200 generations of evolution, the algorithm outputs a prototype set of alternative repair solutions (CANDIDATE REVISION PROTOTYPE SET, CRPS) distributed over the pareto front, typically containing 50-100 non-dominant solutions.

To accelerate convergence, the system introduces a target space dimension reduction technique (Objective Space Dimensionality Reduction, OSDR). When strong correlation (such as correlation coefficient Corr > 0.7) between the value and the acceptance degree is identified through principal component analysis (PRINCIPAL COMPONENT ANALYSIS, PCA), the three-dimensional space is compressed into a 'risk-comprehensive benefit' two-dimensional plane. Meanwhile, a reference point guiding (REFERENCE POINT GUIDANCE, RPG) strategy is adopted, wherein a reference point coordinate (such as [ OAP=0.9, VAD=1.2 and RCL=0.8 ]) is set according to the priority (such as 'acceptance > value > risk') of a target preset by a user, and the algorithm preferentially approximates to a solution region of the reference point direction. All CRPS protocols were accompanied by three-dimensional scoring reports, such as:

Scheme X, 25% value improvement (vad=1.25), 8% risk probability (rcl=0.92), 72% acceptance (oap=0.72);

Scheme Y, 18% improvement in value (vad=1.18), 5% risk probability (rcl=0.95), 85% acceptance (oap=0.85).

At the heart of this stage is performing hard risk filtering (HARD RISK FILTERING, HRF). The system extracts the overall contract risk probability (Overall Contract Risk Probability, OCPRP) corresponding to each scheme from the CRPS, and the calculation formula is OCPRP =1-/(1-ith clause risk probability prp_i).

According to a risk tolerance threshold (Risk Tolerance Threshold, RTT, e.g. less than or equal to 10%) preset by a user, the solution of OCPRP > RTT is directly rejected (e.g. OCPRP =12% of the solution is to be discarded). Single term risk review (Single Clause RISK REVIEW, SCRR) is performed simultaneously, even if the overall risk is controllable, if a term risk impact coefficient RIC is greater than or equal to 4 (e.g., potentially resulting in significant losses) and the risk probability PRP is greater than or equal to 0.3, the scheme will be flagged for warning.

The second layer of filtering is based on Risk-value balance coefficients (Risk-Value Balance Coefficient, RVBC). The coefficients are defined as:

RVBC = (regimen value score VAD)/(risk impact weighting Σ (prp_i×ric_i))

The system sets a dynamic RVBC threshold (RVBC Threshold, RVBCT), the value of which dynamically adjusts with user risk preferences:

RVBCT =2.0 (the value brought by the unit risk is required to be more than or equal to 2 times);

RVBCT =1.2 for risk neutral subscribers;

RVBCT =0.8 for risk preference type users;

For example, one scheme vad=1.25, total risk impact value=0.6, RVBC =2.08 > RVBCT =2.0 is reserved, and the other scheme vad=1.40, risk impact value=1.8, rvbc=0.78 < RVBCT =1.2 is eliminated.

Finally, legal compliance verification (Legal Compliance Verification, LCV) is performed. The system accesses a regulatory knowledge graph (Regulatory Knowledge Graph, RKG) to check if the revision scheme violates the mandatory specification:

1) Subject qualification constraints, such as whether the underwriting clause is outside of the operating range of the opponent after revision;

2) Interest rate legitimacy, i.e. whether the modified financing cost exceeds the LPR (Loan PRIME RATE, loan market quotation interest rate) by 4 times;

3) The special requirements of industry, such as the payment period of the construction engineering contract, are not longer than the upper limit of the supervision of the industry.

The set of preliminary optimization suggestions (PRELIMINARY OPTIMIZATION PROPOSAL SET, POPS) is formed by the three-layer filtered remaining solution, typically reduced to 20-30 high quality solutions. Each solution is accompanied by a risk compliance certificate (Risk Compliance Certificate, RCC), marking the key index passing condition.

The POPS is deeply optimized at this stage by adopting a near-end policy optimization (Proximal Policy Optimization, PPO) algorithm. Considering each revision as a strategy pi, the optimization objective is to maximize the desired jackpot (Expected Cumulative Reward, ECR) ecr=e [ value prize r_val+risk penalty r_risk+acceptance prize r_acc ].

Where r_val=wv× (actual value boost/target value boost), r_risk= -wp×max (0, actual risk probability-risk tolerance threshold), r_acc=wa×oap (acceptance prediction value). The parameters specify wv=value weight, wp=risk penalty coefficient, wa=acceptance weight.

The core iteration flow of PPO comprises:

1) Experience collection (Experience Collection, EC) is based on the current policy pi_old, executing the POPS scheme in a simulated environment, interacting with the opponent agent of the DA-GAN, recording trace data τ= { state s, action a, reward r, new state s' }.

2) Dominance estimation (ADVANTAGE ESTIMATION, AE) calculating a dominance value a_t for each action using a generalized dominance estimator (Generalized Advantage Estimator, GAE);

3) Policy Update (PU):

maximizing the clipping objective function L (θ) =e [ min (r_t (θ) a_t, clip (r_t (θ), 1-epsilon, 1+epsilon) a_t) ];

The parameter states r_t (θ) =new policy probability/old policy probability, ε=shearing coefficient 0.2;

Training oscillations are prevented by constraining the strategy update amplitude.

After k=50 rounds of PPO iterations, the system performs elite strategy distillation (Elite Policy Distillation, EPD):

1) Selecting the Top5 scheme with the highest ECR from the final strategy pool as an elite set;

2) Training a lightweight Policy Network (PN) to simulate the decision mode of elite policies using knowledge distillation (Knowledge Distillation, KD) technology;

3) A batch of schemes was regenerated with PN and its performance verified by DA-GAN.

The final output optimization clause revision suggestion set (Optimized Clause Revision Proposal Set, OCRPS) contains 10-15 schemes, each of which is accompanied by:

a three-dimensional grading radar chart, which is a normalized score of value, risk and acceptance;

Clause change comparison table, namely, the difference mark of the revision clause of the original clause vs;

Simulation execution report, predicted performance probability rise value, expected bad account reduction.

For example, "scheme #7: changing the payment cycle from 90 days to 60 days + increasing 3% of the terms of advance payment → predicting the speed of return to be 25%, decreasing the bad account rate from 5.2% to 3.1%, and the adversary's acceptance probability 81%".

The reinforcement learning-based optimization model simulates a real business negotiating scenario, learning optimal clause revision strategies in millions of virtual negotiations. The model balances the value target (such as the fund recovery rate) and the risk control requirement of the user, generates a revised scheme which not only improves the commercial value but also ensures the legal safety, breaks through the limitation of the traditional manual revision, and explores the optimal clause combination through an intelligent algorithm. The reinforcement learning framework enables the model to have continuous evolution capability and can adapt to the negotiating style and market environment changes of different opponents.

S204, carrying out personalized fitness scoring processing on the optimized clause revision suggestion set by combining the real-time acquired opponent public credit image and market reference data through a lightweight dynamic matching engine to generate a feasible revision strategy sequence ordered according to fitness;

Specifically, distributed feature vectorization can be performed on the optimization clause revision suggestion set to obtain a revision suggestion feature matrix;

The system receives the set of optimization term revision suggestions generated by the preceding steps (including multiple revisions such as "extend payment period to 180 days", "add raw material price fluctuation compensation term", etc.). To achieve efficient computation, a distributed feature vectorization technique is employed by first distributing sets of suggestions in parallel in a computing cluster (e.g., APACHE SPARK clusters), each revision suggestion being broken down into atomic feature units (e.g., "clause type = payment period", "revision direction = extension", "value = 180", "risk level = medium"). Each feature cell is converted to a 256-dimensional Dense Vector (Vector) by a pre-trained financial semantic encoder (fine tuning with BERT architecture), e.g. "extension" is encoded as [0.78, -0.12, 0.45]. The atomic vectors are then hierarchically aggregated through a feature fusion layer, numerical features (such as '180') are standardized and then are directly spliced, category features (such as 'risk level=middle') are mapped into vectors through an embedding layer (Embedding Layer), and relational features (such as 'add compensation terms and association strength of payment cycle extension') are generated into association vectors through a graph attention network (Graph Attention Network, GAT). Finally, each revised proposal is represented as a Feature Vector (Feature Vector) of a fixed length (e.g., 1024 dimensions), all of which constitute a revised proposal Feature matrix (Revision Feature Matrix, RFMatrix) having dimensions [ n×1024] (N is the number of proposals).

In order to ensure the instantaneity and expandability of vectorization, the system adopts a distributed vector calculation pipeline:

Data slicing, namely slicing the proposal set to cluster nodes according to a hash rule, wherein each node independently processes a local proposal subset;

vector parallel generation, wherein each node invokes a locally deployed lightweight coding model (such as a distilled Mini-BERT), so that a central model bottleneck is avoided;

Dynamic dimension alignment, which is to unify vector dimensions of nodes through a shared Feature alignment service (Feature ALIGNMENT SERVICE, FAS), such as automatically filling zero vectors for missing features (such as that a suggestion does not involve a default gold);

Matrix aggregation-summarizing the vectors across nodes by AllReduce algorithm (AllReduce Algorithm), forming global RFMatrix. In the process, the system monitors vector quality indexes (such as vector sparsity Sparsity Ratio and SR; cosine similarity threshold Cosine Similarity Threshold and CST) in real time, if abnormality (such as SR > 30%) is detected, a feature reconstruction flow is triggered, and a semantic encoder is called again to generate supplementary features.

And finally, processing the robustness of the unstructured revision suggestion. For example, when the suggestion contains a ambiguous representation (e.g., "moderately increases interest rate"), the system performs the following operations:

invoking a Fuzzy semantic analyzer (Fuzzy SEMANTIC PARSER, FSP) to quantify "moderate" as a numerical interval (e.g., +1.5% -2.0%);

Based on the history revision case library, supplementing missing features (such as typical risk values corresponding to the interval) by collaborative filtering (Collaborative Filtering, CF);

Confidence scores (Confidence Score, CS) are labeled in RFMatrix for weighting in subsequent steps. The final output RFMatrix serves as the underlying data structure for the fitness score, which corresponds to a machine-computable revision suggestion per row.

Real-time counter-party credit portrait (Counterparty Credit Profile, CCP) is dynamically built by external data pipes, containing three types of core data:

Industrial and commercial credit data such as enterprise credit rating (e.g. AAA/BB), administrative penalty times (ADMINISTRATIVE PENALTY Count, APC), judicial executed amount (Judicial Enforcement Amount, JAA);

Market performance data such as recent year contract performance rate (Contract Fulfillment Rate, CFR), supply chain stability index (Supply Chain Stability Index, SCSI);

public opinion emotion data such as news negative emotion scores (NEGATIVE SENTIMENT Score, NSS, based on LSTM emotion analysis model). These data are converted to structured vectors (e.g., 512-dimensional CCP vectors) by a heterogeneous data fusion engine. The system inputs RFMatrix and CCP vectors into a dynamic weighted vector similarity calculation module, whose core is to calculate the matching degree of each revised proposed feature vector and CCP vector. The improved weighted cosine similarity algorithm (Weighted Cosine Similarity, WCS) is used, and the formula logic is as follows:

basic similarity, namely calculating Cosine Values (CV) of the feature vector and the CCP vector, and the range of [1, 1];

dynamic weight distribution, namely, according to the current risk preference, weights are distributed to different sub-features of the CCP (such as 'judicial executed amount' weight is automatically increased to 0.6 in the litigation high-rise period);

Nonlinear calibration-the CV is mapped to a credit fit score (Credit Adaptation Score, CAS) for the [0,100] interval by a Sigmoid function.

Weight distribution relies on Context-aware rules engine (CARE):

a rule base, which predefines hundreds of weight rules, such as ' contract performance rate ' weight increased by 40% ' if the industry is in a downlink period (triggered by market benchmark data);

And (3) feeding back the loop in real time, wherein each time the CAS is calculated, the follow-up behavior of the counter party is recorded (if the counter party accepts the advice), and the weight rule is adjusted through incremental learning (INCREMENTAL LEARNING). For example, if the history data shows that the acceptance rate of a certain opponent to the "high CAS suggestion" is low, the weight influence of the "public opinion emotion data" is automatically reduced. The computing process employs a streaming framework (e.g., APACHE FLINK) to ensure that the full CAS refresh is completed within 5 seconds after the counter-party data update.

To handle data conflicts (e.g., high credit rating but negative opinion), the system introduces a conflict resolution mechanism:

Step 1, calculating the confidence coefficient of each data source (such as confidence coefficient of credit data=0.9 and public opinion data=0.7);

Step 2, bayesian probability correction (Bayesian Probability Correction, BPC) is carried out on the conflict sub-features, for example, the probability of 'AAA rating' is reduced to 'AA' under the condition of negative public opinion;

and 3, recalculating the CAS based on the corrected characteristic value, and marking conflict marks (Conflict Flag, CF) in the output. Finally, each revision proposal obtains a CAS (e.g. 82.35) that is accurate to the two decimal places, and characterizes the matching degree of the CAS with the credit status of the opponent.

Market benchmark data (Market Benchmark Data, MBD) is accessed in real time to an external source of financial information (e.g., bloomberg, central office) and the core includes:

Macroscopic economic index GDP growth rate, industry scenic index (Industry Prosperity Index, IPI);

financial market parameters, such as equity lending interest rate (e.g., SHIBOR), bond breach rate (Bond Default Rate, BDR);

Regulatory policy changes such as newly issued liability rules severity scores (Regulatory Strictness Score, RSS). The system inputs CAS and MBD into a context aware weight optimization algorithm (Scenario-AWARE WEIGHT Optimization Algorithm, SAWOA) that dynamically assigns a weight ratio of "credit fit" to "market fit". The execution flow is as follows:

scene classification, namely classifying the current market state into preset scenes (such as 'industry expansion period' and 'policy contraction period') through a random forest model (Random Forest Model, RFM);

A weight decision table, which searches a predefined weight distribution table according to the scene category (for example, the weight of the market factor in the compact period is increased to 0.7);

nonlinear optimization, namely fine-tuning weight values by using a gradient descent method (GRADIENT DESCENT, GD) aiming at maximizing the recommended acceptance rate under the similar scene of the history.

SAWOA is a core innovation in a multi-source data coupling mechanism:

time alignment, which is to generate aligned Time stamp data points by Time sequence interpolation (Time-Series Interpolation, TSI) on unsynchronized data (such as quarter GDP and real-Time interest rate);

Cross-dimension association, for example, when the bond violation rate is increased and the rule strictness is increased, automatically triggering a high risk scenario mode to increase the upper limit of the market weight to 0.9;

Black swan event processing, namely dynamically covering a conventional weight rule through an event detector (such as NLP keyword capturing 'war' and 'epidemic'). The algorithm outputs a dynamic weight pair (DYNAMIC WEIGHT PAIR, DWP) for each scenario, e.g., (credit weight: 0.4, market weight: 0.6).

The weighted ensemble suitability score (Weighted Comprehensive Adaptation Score, WCAS) is calculated as follows:

Independently calculating a market fitness score (Market Adaptation Score, MAS), namely inputting MBD and revised suggestion characteristics into a regression model (such as XGBoost), and predicting the feasibility probability (0-100) of the suggestion in the current market;

weighted fusion: WCAS = CAS x w_credit+mas x w_mark, where w_credit+w_mark = 1;

Extreme value correction if WCAS exceeds the historical quantile threshold (e.g., > 95%), then a robustness check is started (Robustness Verification, RV), 1000 disturbance data recalculations are generated with monte carlo simulations (Monte Carlo Simulation, MCS), and the median is taken as the final WCAS. This score directly reflects the overall fit of the revision proposal under the dual constraints of "opponent credit" and "market environment".

The system ranks the revised recommendations according to a weighted overall suitability score (WCAS). To avoid local optima simply caused by decreasing scores, a Multi-criteria ordering framework (Multi-Criteria Sorting Framework, MCSF) is employed:

Main ranking key WCAS score (descending order), e.g., suggestion that WCAS =92.1 takes precedence over 85.4;

A secondary sort key:

implementation costs (Implementation Cost, IC) estimate the resources (e.g., legal costs, time costs) required to execute the suggestion, the lower the cost the higher the rank;

Risk slow release gain (Risk Mitigation Gain, RMG) predicting the magnitude of the decrease in risk value after adoption (e.g., from "high risk" to "medium risk" gain + 30%);

Policy novelty (Strategy Novelty, SN) is based on historical database check weight, avoiding recommending excessively similar old policies. The secondary ranking keys are compared step by a predetermined priority (e.g., RMG > IC > SN) to form a preliminary ranking Sequence (PRELIMINARY RANKED sequences, PRSeq).

The key challenge is to deal with ordering ambiguity of score-close suggestions. The solution comprises the following steps:

fuzzy clustering (Fuzzy Clustering, FC) clustering the advice with WCAS difference value <5 into a same class group, and sequencing the groups according to risk gain;

Pareto front screening (Pareto Frontier Filtering, PFF) for co-score advice, selecting a scheme for preferential display at the pareto optimal front (Pareto Optimal Frontier, POF) in the "WCAS-implementation cost-risk gain" three-dimensional space;

manual rule injection-allowing the user to predefine mandatory ordering rules (e.g. "advice concerning payment periods must be ordered by the first 3 bits"). The ordering process generates a sequence of layer-level structures, such as:

tier 1 (WCAS. Gtoreq.90) advice a (WCAS =92.1, rmg= +40%) →advice B (WCAS =90.5, rmg= +35%);

Tier 2 (85≤ WCAS < 90) recommended C (WCAS =87.2, rmg= +28%).

Each suggestion is accompanied by a ranking basis report (Ranking Justification Report, RJR) that enhances interpretability.

The system implements a sorting stability guarantee mechanism:

Inputting disturbance analysis, namely adding plus or minus 5% noise to the original data, repeatedly sequencing for 10 times, and marking the data as an unstable sequence if the ranking variation rate is more than 20%;

Backtracking recalibration, namely, generating a consensus sequence for an unstable sequence by adopting a Borda counting method (Borda Count Method, BCM) to synthesize a plurality of sequencing results;

real-Time buffer optimization, namely buffering PRSeq of high-frequency access counter-party data (such as large enterprise CCP) and setting Time-To-Live (TTL=300 seconds) To reduce repeated calculation. The output sequence provides a baseline input for the subsequent dynamic matching engine.

The lightweight dynamic matching engine (LIGHTWEIGHT DYNAMIC MATCHING ENGINE, LDME) is a core optimization component that iteratively refines PRSeq through a triple mechanism:

Mechanism 1 user implicit feedback learning

The buried point tracks the user behavior by recording the stay time (DWELL TIME, DT) of the user on the interface, the recommended Expansion Count (EC), and the manual adjustment sequencing (Manual Rank Adjustment, MRA);

Converting the behavior into a weight signal (e.g., DT >30 seconds is considered as "aggressive signal" +0.1 weight) by an implicit feedback model (Implicit Feedback Model, IFM);

Dynamically enhancing the ranking of the suggestion of interest (e.g., the suggestion of original rank 5 is raised to 2 after ec=3 times).

Mechanism 2 external event real-time response

Establishing an event listener (EventListener) to subscribe to a key data source (such as an industrial and commercial change, judicial announcement);

When a related event (such as newly executed information of the opposite party) is detected, sequence Re-evaluation (SR) is immediately triggered:

Updating a judicial executed amount (JAA) in the CCP;

recalculating the CAS of the affected suggestion WCAS;

a change flag (CHANGE FLAG, CF) is inserted for the suggestion that the ordering changes >3 bits.

For example, some suggestion may be WCAS from 85 to 72 due to deterioration of the counter-party credit, with the ranking automatically dropping from position 3 to position 8.

Mechanism 3 Cross-client collaborative filtering

An anonymity policy pool (Anonymous Strategy Pool, ASP) collects revision suggestions of history adoption and results thereof (e.g. "accept/reject", "success rate of performance");

when a new customer scene matches a similar case (KNN neighbor algorithm, k=5), injecting a strategy with high success rate into the current sequence;

for example, the similarity between the current customer and a manufacturing enterprise is detected to be 80 percent, and the performance rate of the raw material compensation clause of the enterprise is detected to be 95 percent, so that the similar suggestion ranking in the current sequence is improved.

The engine ultimately outputs a feasible revision policy sequence (Feasible Revision Strategy Sequence, FRSSeq) featuring:

With dynamic weight labels (such as' recommended index:;

Support the hierarchical folding display (Tier 1/2/3 according to WCAS intervals);

each policy is accompanied by an optimized path trace back (e.g. "promote 2 bits due to user attention"). This sequence is used directly by the subsequent counterfactual deduction module.

The system integrates data such as credit rating, historical performance records and the like of the opponent in real time, and evaluates the matching degree of each revision suggestion and a specific opponent by combining the current market interest rate, industry scene air strength and other reference indexes. The lightweight engine ensures millisecond response speed, avoids standardized advice of 'one-cut', and provides a personalized solution for specific trade opponents and market environments. Real-time data access ensures timeliness and pertinence of the advice.

S205, based on the feasible revision strategy sequence, interactive counterfactual deduction is carried out, a liability performance probability change curve and expected damage distribution under a preset external situation after different revision strategies are adopted are visually displayed for a client, and a final clause revision scheme and a matched risk slow-release measure suggestion are determined according to feedback selection of a deduction result by a user and are used as output of digital value-added service.

Specifically, multi-scenario counterfactual generation can be implemented for a feasible revision policy sequence, and a deduction scenario set is created;

The system receives an ordered sequence of viable revision strategies (Feasible Revision Strategy Sequence, FRSS) containing clause revisions (e.g., interest rate adjustment amplitude, vouch-for mode changes, repayment cycle resets, etc.) in descending order of fitness. The Multi-scenario counterfactual generation (Multi-Scenario Counterfactual Generation, MSCG) module first parses the core revision point (Core Revision Point, CRP) of each policy and associates a pre-set external scenario parameter library (External Scenario Parameter Library, ESPL). ESPL stores macro-economic and industry risk factor combinations predefined by financial engineering teams, including three types of scenario dimensions:

A benchmark scenario (Baseline Scenario, BS) based on current market consensus predictions (e.g., GDP growth rate cpi_bs=2.5%, CPI refers to consumer price index);

Pressure Scenario (SS) such as "sharp sudden rise 300 base point" (BPS, base point unit, 1 base point=0.01%), "industry demand atrophy 20%";

extreme Scenarios (ES) such as "counter-party credit rating down three steps" (e.g. from AA to BBB), "bulk commodity price volatility doubling" (vol_es=40%, vol refers to volatility);

Each scene is assigned a unique Scene Code (SC), such as BS01, SS05, ES12.

The counterfactual generation engine employs a conditional variation self-encoder (Conditional Variational Autoencoder, CVAE) technique, the inputs of which are a revised policy feature vector (Revision Strategy Feature Vector, RSFV) and a scene parameter vector (Scenario Parameter Vector, SPV). CVAE encoder (Encoder) compresses RSFV and SPV into Latent Variables (LV) and Decoder (Decoder) reconstructs a counterfactual scene description (Counterfactual Scenario Description, CSD) based on the LV. For example, for a "extend payback period to 36 months" strategy, generated in a rate ramp up scenario (SS 05), "if the central row benchmark rate is raised 300BPS, the counter-party mobile funds coverage (lcr_ss=80%, LCR means mobile coverage) falls to a warning line, possibly resulting in a 24 th month payback delay). The scenario generation process introduces a semantic consistency checker (Semantic Consistency Validator, SCV) to ensure economic and logical rationality (e.g., the cost of financing for an enterprise must increase as interest rate increases).

The final derived scene set (Scenario Set for Deduction, SSD) is in a standardized data structure, each record containing a scene number (Scenario ID, SID), an associated revision policy ID (STRATEGY ID, STID), scene description text (Scenario Description Text, SDT), a quantization parameter vector (Quantitative Parameter Vector, QPV). For example, in the record of sid=ss05_st 023, QPV contains key parameters of interest rate rise Δr=3.0%, hand nutrient gain reduction Δrev= -15%, industry violation rate reference value pd_base=5.2% (PD refers to violation probability). SSD is de-duplicated through a scene similarity clustering (Scenario Similarity Clustering, SSC) algorithm, so that the difference threshold value between scenes is ensured to be more than 30 percent (calculated through Jaccard distance), and finally an deduction set containing 50-200 unique scenes is formed.

The liability probability simulator (Debt Performance Probability Simulator, DPPS) reads the scenario data from SSD one by one, and builds a dynamic performance assessment model (Performance Assessment Model, PAM). The core of the model is a three-layer computing architecture:

Cash Flow Layer (CFL) generating a monthly net Cash Flow sequence (Net Cash Flow Sequence, NCFS) based on revised payment plans (e.g., principal repayment tables), opponent business predictions (e.g., quarter rate of increase in revenue g_q), cost structure (fixed cost rate FCR = 35%);

A risk conducting layer (Risk Transmission Layer, RTL) mapping risk factors (such as Interest rate Δr, commodity price volatility Vol) in QPV to opponent repayment capability indicators, such as Interest guarantee multiples (icr_t=ebit_t/interest_t, EBIT refers to pre-tax profit) by a vector autoregressive model (Vector Autoregression Model, VAR);

The violation criteria layer (Default Criterion Layer, DCL) sets ICR <1.5 or the flow ratio (CR_t) <1.0 as a technical violation trigger condition.

The monte carlo simulation (Monte Carlo Simulation, MCS) engine performs 10,000 random samples for each scenario. Key random variables include:

-a normal distribution N (μ=Δrev, σ=rv_std) to the hand camp rate (Revenue Volatility, RV), where rv_std is taken from industry history data;

market rate variation (Δr_t) to band Jump diffusion process (Jump-Diffusion Process, JDP), base diffusion coefficient σ_diff=0.2, jump frequency λ_jump=0.05 (i.e. 5 large fluctuations are expected each year);

Each sample generates a performance state path (Performance Status Path, PSP) and records icr_t, cr_t values and Default Flag (DF) at various time points from the start month (t=0) to the expiration month (t=t) of the contract.

After the simulation is completed, all PSPs are aggregated to generate probability change curve data (Probability Curve Data, PCD). The data contains two core curves:

An accumulated performance probability curve (Cumulative Performance Probability Curve, CPPC) that calculates the path occupancy without violations up to monthly t, the formula being CPPC (t) =1- Σdf_t/10,000;

A marginal breach probability curve (Marginal Default Probability Curve, MDPC) is calculated for a new breach path duty cycle of t new breach per month, MDPC (t) =df_t/10,000.

Each curve is stored as a time series array by scene number (SID) and policy number (StID), e.g., CPPC data for sid=ss05_st 023 is: [ t=1:99.2%, t=2:97.8% ], t=36:82.3% ]. The data precision is reserved to the next bit after the decimal point.

The Expected damage-and-benefit Engine (EPLE) interfaces the PCD with real-time market benchmark data (Market Benchmark Data, MBD). MBD contains three types of inputs:

a Risk-Free Rate Curve (RFRC) taken from national debt rates (e.g., 1 year YTM _1y=2.3%, YTM refers to an expired Rate);

Credit risk Premium (CREDIT RISK Premium, CRP) based on a opponent industry classification (e.g., industry board Premium crp_ind=150 BPS);

capital cost parameters (Funding Cost Parameter, FCP) including financing difference (Funding Spread, fs=80 BPS), operating cost rate (Operational Cost Ratio, ocr=0.7%);

The engine first calculates the Discount Factor (DF_t) sequence DF_t=1/(1+RFRC_t+CRP+FS)/(t/12).

The dynamic modeling process is performed in three steps:

Step 1 cash flow discount (Discounted Cash Flow, DCF)

Normal performance path, namely, discount each cash inflow (such as principal repayment P_t and interest I_t) in NCFS according to DF_t;

the default path is that according to the default time t_d, recovery Residual Value (RV) is discounted according to DF_ { t_d } and RV=collateral estimation value×recovery Rate (RR), and RR takes industry average Value of 45%;

Step 2, profit and loss calculation (P & L Calculation)

Loss value of single sample (P & l_i) = Σ (discount cash in) -initial bond principal (Outstanding Principal, OP);

Step 3 distribution construction (Distribution Construction)

The P & L_i of 10,000 samples were aggregated to form a damage distribution histogram (P & L Distribution Histogram, P & DH), and the Bin Count (BC) was set to 50.

The resulting Profit-loss profile (Profit-Loss Distribution Plot, PLDP) contains three key visualizations:

Probability density curve (Probability Density Curve, PDC) of P & DH fitted smoothly, showing probability of occurrence of different loss

Risk indicator labeling (RISK METRIC analysis, RMA) is marked on the graph:

expected profit and loss (Expected P & L, ep=Σ (P & l_i×prob_i), e.g., ep= ++ $1.2M;

Maximum deficit at Risk Value (Value at Risk, vaR): 95% confidence (e.g. var95= - $0.8M);

tail expected loss (Expected Shortfall, ES) the average loss when the loss exceeds VaR95 (e.g., ES95 = - $1.5M);

And a scene contrast layer (Scenario Comparison Layer, SCL) for superimposing PDC of a plurality of scenes with different colors, for example, a blue curve represents a reference scene (EP_BS= + $1.5M), and a red curve represents a pressure scene (EP_SS= - $0.3M).

The system visually presents PLDP and CPPC curves to the customer through an interactive deduction console (INTERACTIVE DEDUCTION CONSOLE, IDC). The console provides three types of feedback tools:

Parameter adjustment slide bars (PARAMETER ADJUSTMENT SLIDER, PAS) allow the user to modify key assumptions in real time, such as:

Collateral Recovery (RR) was adjusted from 45% to 60%;

up-regulating the increase rate (Revenue Growth Rate, RGR) of the hand-made nutrient from-15% to-10%;

A policy weight Selector (STRATEGY WEIGHT Selector, SWS) reorders the policies in the FRSS, e.g., to increase the weight of the "Add-of-guarantee clause" from 0.3 to 0.7;

a scene attention mark (Scenario Attention Marker, SAM) for marking a scene of interest (e.g., a mark sid=es12 is a high risk scene);

User operation triggers Real-time recalculation (Real-Time Recalculation, RTR), with response delay controlled to within 2 seconds.

A decision optimization engine (Decision Optimization Engine, DOE) performs a three-stage optimization based on the feedback data:

stage 1 constraint condition update

Adding constraint in an optimization model according to a high risk scene marked by a user (such as ES 12), wherein VaR95 in the ES12 scene is more than or equal to- $1M;

Stage 2 object function reconstruction

Substituting the weight adjusted by the user (such as 0.7 of the guaranteed money weight) into the multi-objective function:

max [ alpha X EP+beta X performance probability-gamma X VaR95], wherein alpha, beta, gamma are weight parameters;

stage 3 Pareto front search

The revised strategy solution space is searched for a pareto optimal solution set (Pareto Optimal Set, POS) that satisfies the new constraint using a Non-dominant ordering genetic algorithm (Non-dominated Sorting Genetic Algorithm, NSGA-II).

The engine outputs a final clause revision scheme (Final Revision Plan, FRP) whose determination logic is:

screening the highest 3 candidate schemes of the EP from the POS;

Calculating a scenario robustness score (Scenario Robustness Score, SRS) for each scenario based on the user-tagged scenarios (SAM data);

srs=Σ (scene attention x probability of performance in the scene), the attention being determined by the number of user marks;

the scheme with SRS being greater than or equal to the threshold (e.g., 80 minutes) and highest EP ranking is selected.

The final solution contains specific term revisions (e.g., "adjust interest rate from LIBOR+200BPS to LIBOR+150BPS, append 30% cash deposit") and automatically generates revised recommendations with legal term templates.

A final output risk slow release advice package (Risk Mitigation Package, RMP) comprising:

Core hedging measures (Core Hedging Measures, CHM) 3-5 cost-effective measures (e.g. "purchase counter party 1-year CDS, nominal principal $10m, cost $150k");

Emergency triggering terms (Contingency Trigger Clause, CTC) suggesting automated response terms to join the contract, e.g. "if the opponent's credit rating drops to BB+, automatically initiate additional vouching procedures";

A continuous monitoring scheme (Continuous Monitoring Plan, CMP) that designates an index to be tracked (e.g., month data of the flow rate of the counter-party), a monitoring frequency, and a threshold alert rule;

All outputs are integrated into a digital service Report (DIGITAL SERVICE Report, DSR), and are delivered to a customer after being stored through a blockchain, so that the value added service closed loop is completed.

By means of Monte Carlo simulation technology, the system can show the performance probability and financial impact of each revision under different economic scenarios (such as interest rate fluctuation and industry degradation). The user can interactively adjust parameters, visually compare the advantages and disadvantages of various schemes, convert complex legal terms into visual business impact analysis and assist the user in making data-driven decisions. The interactive design enhances the transparency of the service and the user's feeling of participation.

It can be seen that the method comprises the steps of receiving an original file set of the credited debt uploaded by a customer, generating a structured clause element set containing clause types, core elements and expression mode characteristics, generating a risk quantification label of each key clause based on the structured clause element set, inputting the structured clause element set and the risk quantification label thereof into a pre-trained clause value-risk collaborative optimization model, generating a set of optimized clause revision suggestion sets for maximizing a user set value target under the premise of controllable risk, generating a feasible revision strategy sequence ordered according to the adaptation degree for the optimized clause revision suggestion sets, determining a final clause revision scheme and matched risk slow-release measure suggestion based on the feasible revision strategy sequence, and outputting the final clause revision scheme and the matched risk slow-release measure suggestion as digital value-added service, so that the accuracy and decision efficiency of the credited debt management can be improved.

Still another embodiment of the present invention provides a digital value added service system based on AI-based credited liability requirement analysis, see fig. 3, which may include:

the receiving module 301 is configured to receive an original document set of liability and liability uploaded by a client, and perform semantic deconstructing and element extraction on key terms in the document by using a deep semantic analyzer based on countermeasure training, so as to generate a structured term element set including term types, core elements and expression features;

The evaluation module 302 is configured to invoke a dynamically associated industry risk event library based on the structured clause element set, perform clause potential risk probability calculation and risk influence degree evaluation processing according to similarity matching between element features and risk event cases, and generate a risk quantification label of each key clause;

The optimizing module 303 is configured to input the structured clause element set and the risk quantification label thereof into a pre-trained clause value-risk collaborative optimizing model, perform multiple rounds of iterative optimization simulation processing according to the current clause element state and a preset target, and generate a set of optimized clause revision suggestion sets that maximize a user set value target on the premise of controllable risk, where the collaborative optimizing model trains the acceptance degree of the clause modification and the final execution result of the opponent under different negotiation strategies by simulating the reinforcement learning frame;

The matching module 304 is configured to revise the suggestion set of the optimization clause, combine the real-time acquired public credit image of the opponent and the market benchmark data, perform personalized fitness scoring processing through a lightweight dynamic matching engine, and generate a feasible revision strategy sequence ordered according to the fitness;

The determining module 305 is configured to perform interactive counterfactual deduction based on the feasible revision policy sequence, visually display a liability performance probability change curve and an expected damage distribution under a preset external scenario after different revision policies are adopted for a client, and determine a final clause revision scheme and a matched risk slow-release measure suggestion as output of the digital value-added service according to feedback selection of a deduction result by a user.

The embodiment of the invention also provides a storage medium, in which a computer program is stored, wherein the computer program is configured to perform the steps of any of the method embodiments described above when run.

Specifically, in the present embodiment, the above-described storage medium may be configured to store a computer program for executing the steps of:

The present invention also provides an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.

Specifically, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.

Specifically, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:

The construction, features and effects of the present invention have been described in detail with reference to the embodiments shown in the drawings, but the above description is only a preferred embodiment of the present invention, but the present invention is not limited to the embodiments shown in the drawings, all changes, or modifications to the teachings of the invention, which fall within the meaning and range of equivalents are intended to be embraced therein, are intended to be embraced therein.

Claims

1. A digital value-added service method for AI-based debt demand analysis, characterized in that the method includes:

Receive the original debt and credit document set uploaded by the customer, and use the deep semantic parser based on adversarial training to perform semantic deconstruction and element extraction on the key clauses in the document to generate a structured clause element set containing clause type, core elements, and expression characteristics;

Based on the structured clause element set, the dynamically associated industry risk event database is called, and based on the similarity matching between the element characteristics and the risk event cases, the potential risk probability of the clauses and the risk impact degree are calculated, and a risk quantitative label for each key clause is generated;

The structured clause element set and its risk quantification labels are input into a pre-trained clause value-risk collaborative optimization model. Based on the current clause element status and preset goals, multiple rounds of iterative optimization simulation are performed to generate a set of optimized clause revision suggestions that maximize the user-set value goals while ensuring controllable risks. The collaborative optimization model is based on a reinforcement learning framework and is trained by simulating the counterparty's acceptance of clause modifications and the final execution results under different negotiation strategies.

The set of recommended revisions to the optimized terms is combined with the counterparty's public credit profile and market benchmark data obtained in real time, and a lightweight dynamic matching engine is used to perform personalized suitability scoring, generating a sequence of feasible revision strategies sorted by suitability.

Based on the feasible revision strategy sequence, interactive counterfactual deduction is conducted to visually display to customers the debt fulfillment probability change curve and expected profit and loss distribution under preset external scenarios after adopting different revision strategies. Based on the user's feedback on the deduction results, the final terms revision plan and supporting risk mitigation measures are determined as the output of digital value-added services.

2. The method according to claim 1 is characterized in that the method receives the original debt and credit document set uploaded by the customer, performs semantic deconstruction and element extraction on the key terms in the document through a deep semantic parser based on adversarial training, and generates a structured term element set containing term type, core elements, and expression characteristics, including:

Multimodal adversarial feature extraction is performed on the original debt and credit document set uploaded by the customer to obtain an adversarial enhanced feature vector set that integrates text semantics and document layout;

Based on the adversarially enhanced feature vector set, a sequence labeling model driven by a bidirectional attention mechanism is used to detect clause boundaries and obtain a preliminary clause segmentation map.

Apply graph convolutional networks to model the element relationships of the preliminary clause segmentation graph and generate a clause element relationship graph;

The semantic parser optimized through adversarial training is used to deconstruct the clause element relationship graph and generate a structured clause element set.

3. The method according to claim 2 is characterized in that, based on the structured clause element set, the dynamically associated industry risk event library is called, and the potential risk probability of the clause and the risk impact degree are calculated and evaluated based on the similarity between the element characteristics and the risk event cases, and a risk quantitative label for each key clause is generated, including:

Based on the structured clause feature set, heterogeneous graph embedding representation learning is performed to obtain the multi-dimensional vector representation of the clause features;

Call the dynamic risk event database and perform event similarity graph matching on the multi-dimensional vector representation of clause elements to obtain a set of similar risk event cases;

Based on a collection of similar risk event cases, Bayesian-Monte Carlo risk probability modeling is performed to obtain potential risk probability values;

Based on the potential risk probability value and event case impact data, a multi-dimensional risk impact fusion assessment is conducted to generate a risk quantification label for each key clause.

4. The method according to claim 3, characterized in that the structured clause element set and its risk quantification labels are input into a pre-trained clause value-risk collaborative optimization model, and multiple rounds of iterative optimization simulation processing are performed based on the current clause element status and preset goals to generate a set of optimized clause revision suggestions that maximize the user-set value goals under the premise of controllable risks. The collaborative optimization model is based on a reinforcement learning framework and is trained by simulating the counterparty's acceptance of clause modifications and the final execution results under different negotiation strategies, including:

According to the structured clause element set and its risk quantification label, the multi-objective reinforcement learning state space is initialized to obtain the initial strategy space;

Based on the initial strategy space, a dual-agent generative adversarial network is used to simulate the adversarial negotiation process and obtain the predicted value of the opponent's acceptance.

Based on the counterparty acceptance prediction value, the value-risk Pareto frontier is optimized to obtain a prototype set of alternative revision plans;

Filter the prototype set of alternative revision plans through risk threshold constraints to select a preliminary set of optimization suggestions;

Based on the preliminary optimization suggestion set, a set of optimization clause revision suggestion sets is generated through multiple rounds of policy gradient reinforcement learning iterations.

5. The method according to claim 4, characterized in that the set of optimized clause revision suggestions is combined with the counterparty's public credit profile and market benchmark data obtained in real time, and personalized compatibility scoring is performed through a lightweight dynamic matching engine to generate a sequence of feasible revision strategies sorted by compatibility, including:

Perform distributed feature vectorization on the set of optimized clause revision suggestions to obtain a revision suggestion feature matrix;

Based on the revised proposed feature matrix and the real-time counterparty credit profile, a dynamic weighted vector similarity calculation is performed to obtain a credit suitability score;

Integrate the credit suitability score and market benchmark data, run a scenario-aware weight optimization algorithm, and calculate a weighted comprehensive suitability score;

Rank the strategies according to the weighted comprehensive fitness scores to obtain a preliminary ranking sequence;

A lightweight dynamic matching engine is applied to perform real-time feedback optimization on the preliminary sorting sequence and output a feasible revision strategy sequence.

6. The method according to claim 5, characterized in that the interactive counterfactual deduction based on the feasible revision strategy sequence is performed to visually display to the customer the debt performance probability change curve and expected profit and loss distribution under preset external scenarios after adopting different revision strategies, and based on the user's feedback on the deduction results, the final terms revision plan and supporting risk mitigation measures are determined as the output of the digital value-added service, including:

Perform multi-scenario counterfactual generation for a sequence of feasible revision strategies to create a set of deduction scenarios;

Based on the deduction scenario set, Monte Carlo simulation of debt performance probability is carried out to obtain probability change curve data;

Based on the probability change curve data and market benchmark data, dynamic modeling of expected profit and loss is carried out to obtain a profit and loss distribution chart;

Based on the profit and loss distribution chart and real-time user feedback, interactive decision optimization is carried out to determine the final terms revision plan and supporting risk mitigation measures.

7. A digital value-added service system for AI-based debt demand analysis, characterized in that the system includes:

The receiving module is used to receive the original debt and credit document set uploaded by the customer. It uses a deep semantic parser based on adversarial training to perform semantic deconstruction and element extraction on the key terms in the document, and generate a structured term element set containing the term type, core elements, and expression characteristics.

An evaluation module is used to call a dynamically associated industry risk event library based on the structured clause element set, calculate the potential risk probability of the clause and evaluate the risk impact based on the similarity between the element characteristics and the risk event cases, and generate a risk quantification label for each key clause;

An optimization module, configured to input the structured clause element set and its risk quantification labels into a pre-trained clause value-risk collaborative optimization model, perform multiple rounds of iterative optimization simulation based on the current clause element status and preset objectives, and generate a set of optimized clause revision suggestions that maximize the user-set value objectives while maintaining controllable risks. The collaborative optimization model is based on a reinforcement learning framework and is trained by simulating the counterparty's acceptance of clause modifications and the final execution results under different negotiation strategies;

A matching module is used to combine the set of optimized clause revision suggestions with the counterparty's public credit profile and market benchmark data obtained in real time, perform personalized compatibility scoring through a lightweight dynamic matching engine, and generate a sequence of feasible revision strategies sorted by compatibility;

The determination module is used to conduct interactive counterfactual reasoning based on the feasible revision strategy sequence, and visually display to customers the debt performance probability change curve and expected profit and loss distribution under preset external scenarios after adopting different revision strategies. Based on the user's feedback on the reasoning results, the final terms revision plan and supporting risk mitigation measures are determined as the output of digital value-added services.

8. The system according to claim 7, wherein the receiving module is specifically configured to:

9. A storage medium, characterized in that a computer program is stored in the storage medium, wherein the computer program is configured to execute the method according to any one of claims 1 to 6 when running.

10. An electronic device comprising a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 6.