Hybrid large model cascaded intelligent decision method

Abstract

The application discloses a mixed large model cascade intelligent decision method, and relates to the technical field of artificial intelligence decision-making. The method comprises the following steps: receiving demand data of a complex decision-making scenario, disassembling a decision-making dimension, defining precision and risk tolerance, constructing a decision-making risk matrix, and generating a hierarchical risk type decision-making demand topology conformation; a large model cascade-risk oriented matching architecture is used to call a first large model and a second large model to perform cascade operation, redundant links are removed, and a risk controllable type refined decision-making intermediate feature code table is generated; the decision-making risk reciprocating verification-optimization architecture is used to evaluate risk threshold adaptability, reversely optimize links exceeding the threshold, complete and deduce logical faults, and generate a final intelligent decision-making correlation topology graph; execution effect data of the final intelligent decision-making correlation topology graph is collected, a decision-making risk matrix is updated, and a decision-making template is deposited into a decision-making feature code knowledge base, and iterative optimization of decision-making capability is completed. The method improves decision-making pertinence and risk controllability.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence decision-making technology, and more specifically, to a hybrid large-model cascaded intelligent decision-making method. Background Technology

[0002] In complex decision-making scenarios such as financial portfolio optimization, large-scale engineering project management, and smart urban traffic scheduling, the decision-making process often involves multiple objectives, multiple constraints, and high uncertainty risks, placing stringent demands on the accuracy, risk controllability, and execution efficiency of the decision-making solutions. In such scenarios, the decision-making dimensions are complex and interconnected, requiring a balance between achieving core objectives, balancing secondary indicators, and mitigating potential risks. Traditional decision-making technologies are no longer sufficient to meet the intelligent decision-making needs across the entire process.

[0003] Existing technical solutions process data across all decision dimensions using a unified model architecture, employ fixed model invocation strategies to perform inference operations, generate decision results based on preset process templates, and then conduct post-evaluation using only a single-dimensional risk indicator. This approach fails to dimensionally define decision requirements, relies on the same performance model for all decision-making stages, and lacks dynamic inference path optimization and logical verification mechanisms.

[0004] The existing technical solution has significant drawbacks: due to the lack of a hierarchical processing and risk correlation modeling mechanism for decision dimensions, it is impossible to quantify the risk transmission relationship between different dimensions, which makes the decision results prone to local optima rather than global optima due to dimensional coupling conflicts; at the same time, the fixed model calling and inference process lacks targeted optimization, which not only wastes computing resources or causes insufficient accuracy in key links, but also makes it difficult to identify redundant links and logical gaps in the decision chain, and cannot achieve continuous iteration of decision-making capabilities through execution effect feedback, ultimately leading to uncontrolled decision risks, low efficiency and insufficient adaptability. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a hybrid large-scale model cascaded intelligent decision-making method to at least alleviate these problems.

[0006] The technical solutions provided in this application are as follows:

[0007] A hybrid large-scale model cascaded intelligent decision-making method includes: Step 1: Receiving demand data from complex decision-making scenarios, decomposing decision dimensions and defining accuracy and risk tolerance through a hierarchical decision demand-risk modeling architecture, constructing a decision risk matrix to mark the risk correlation of dimensions, and generating a hierarchical risk-type decision demand topology; Step 2: Based on the hierarchical risk-type decision demand topology, using a large-scale model cascade-risk-oriented matching architecture to call the first and second large-scale models to perform cascaded operations, eliminating redundant links through a decision chain pruning operation framework, and generating a risk-controllable refined decision intermediate feature code table; Step 3: Based on the risk-controllable refined decision intermediate feature code table, evaluating the risk threshold adaptability through a decision risk iterative verification-optimization architecture, reverse optimizing excessive links, and completing logical gaps to generate a final intelligent decision correlation topology; Step 4: Collecting the execution effect data of the final intelligent decision correlation topology, adjusting the architecture parameters and pruning rules through a cascaded model weight-decision chain dual optimization adaptation mechanism, updating the decision risk matrix, and depositing decision templates into the decision feature code knowledge base to complete the iterative optimization of decision-making capabilities.

[0008] The technical solution provided in this application has the following technical advantages: First, this approach decomposes decision-making dimensions and defines precision and risk tolerance using a hierarchical decision-making needs-risk modeling architecture. A decision risk matrix is ​​then constructed to mark the risk relationships between dimensions, generating a hierarchical risk-based topological construct of decision-making needs. Traditional techniques fail to structurally layer decision-making needs, making it impossible to distinguish between core and secondary dimensions or quantify the risk relationships between dimensions, leading to one-sided decision-making. This solution, through dimensional decomposition and risk matrix construction, transforms complex needs into a structured topological construct, clearly defining the precision requirements, risk tolerance, and relationships of each dimension. This addresses the problem of coarse-grained decision-making needs analysis at its source, providing a structured foundation for subsequent precise decision-making, making decisions more targeted, and reducing local optima problems caused by dimensional coupling conflicts.

[0009] Secondly, based on the hierarchical demand topology, a large-model cascading-risk-oriented matching architecture is adopted to call the first and second largest models for cascading operations, and redundant links are eliminated through a decision chain pruning framework. Traditional techniques use a single model or a fixed calling strategy, which either results in slow response and wasted computing power or insufficient accuracy, and the redundancy of the decision chain leads to low efficiency. This solution realizes the cascading call of models with different performance based on dimensional attributes, so that the core dimension processing takes into account both real-time performance and basic accuracy, while secondary dimensions and risk assessment are deeply optimized. At the same time, the pruning framework eliminates invalid derivation links, which not only rationally allocates computing resources and reduces unnecessary computational overhead, but also improves the efficiency of decision reasoning, solving the problems of rigid model calling and lengthy decision chains.

[0010] Furthermore, by repeatedly verifying and optimizing the decision-making risk architecture, the risk threshold adaptability of intermediate results is assessed. This allows for reverse optimization of excessive steps and the completion of logical gaps in the derivation process. Traditional technologies lack a closed-loop verification mechanism in the decision-making process, relying solely on post-event evaluation. This makes it difficult to correct high-risk paths in a timely manner, and logical gaps can easily lead to decision failure. This solution establishes a repeated verification and reverse optimization mechanism, proactively identifying excessive risk steps and adjusting key parameters during the decision-making process. Simultaneously, it fills in logical gaps, ensuring that the decision-making outcome is risk-controllable and logically closed-loop. This significantly improves the robustness and reliability of the decision-making process and solves the technical bottleneck of uncontrollable decision-making.

[0011] Finally, execution performance data is collected, and the architecture parameters and pruning rules are adjusted through a cascaded model weight-decision chain dual optimization and adaptation mechanism. This updates the risk matrix and accumulates decision templates. Traditional technologies lack execution performance feedback and adaptive adjustment mechanisms, hindering continuous system evolution. This solution achieves dynamic optimization of model call priority, risk weights, and pruning rules through data feedback. Simultaneously, successful cases are transformed into reusable templates, enabling the system to accumulate experience, iterate continuously, and gradually improve its decision-making capabilities. This solves the problem of insufficient adaptability and the inability to "become better with use" in traditional systems. Attached Figure Description

[0012] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0013] Figure 1 This is a flowchart of a hybrid large-scale model cascaded intelligent decision-making method according to an embodiment of the present invention. Detailed Implementation

[0014] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. Various aspects are provided by way of explanation and not limitation of the invention. Indeed, those skilled in the art will recognize that modifications and variations can be made to the invention without departing from its scope or spirit. For example, a feature represented or described as part of one embodiment may be used in another embodiment to produce yet another embodiment. Therefore, it is desirable that the invention encompass such modifications and variations falling within the scope of the appended claims and their equivalents.

[0015] In the description of this invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," and "bottom," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and do not require the invention to be constructed and operated in a specific orientation; therefore, they should not be construed as limitations on the invention. The terms "connected," "linked," and "set up" used in this invention should be interpreted broadly. For example, they can refer to a fixed connection or a detachable connection; they can refer to a direct connection or an indirect connection through intermediate components. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0016] like Figure 1 As shown, this invention provides a hybrid large-model cascaded intelligent decision-making method, including: Step 1: Receiving demand data from complex decision-making scenarios, decomposing decision dimensions and defining accuracy and risk tolerance through a hierarchical decision demand-risk modeling architecture, constructing a decision risk matrix to mark the risk correlation of dimensions, and generating a hierarchical risk-type decision demand topology; Step 2: Based on the hierarchical risk-type decision demand topology, using a large-model cascade-risk-oriented matching architecture to call the first and second large models to perform cascaded operations, eliminating redundant links through a decision chain pruning operation framework, and generating a risk-controllable refined decision intermediate feature code table; Step 3: Based on the risk-controllable refined decision intermediate feature code table, evaluating the risk threshold adaptability through a decision risk iterative verification-optimization architecture, reverse optimizing excessive links, and completing logical gaps to generate a final intelligent decision correlation topology; Step 4: Collecting the execution effect data of the final intelligent decision correlation topology, adjusting the architecture parameters and pruning rules through a cascaded model weight-decision chain dual optimization adaptation mechanism, updating the decision risk matrix, and depositing decision templates into the decision feature code knowledge base to complete the iterative optimization of decision-making capabilities.

[0017] Optionally, step 1 includes: Step 11, decomposing the demand data of complex decision-making scenarios into dimensions, dividing them into core decision dimensions, secondary dimensions, and risk dimensions to generate a decision dimension topology; Step 12, defining the decision accuracy requirements and risk tolerance thresholds for each decision dimension to generate a dimension decision constraint correlation matrix; Step 13, constructing a decision risk matrix, marking the risk correlation relationships between each dimension, and generating a hierarchical risk-type decision demand topology based on the decision dimension topology and the dimension decision constraint correlation matrix, combined with the decision risk matrix. Optionally, step 11 includes: Step 111, performing semantic parsing and feature extraction on the decision objectives, constraints, and business data in the demand data to generate a decision demand feature profile; Step 112, performing dimension clustering based on the business correlation of the decision demand feature profile, dividing them into core dimensions, secondary dimensions, and risk dimensions to generate a decision dimension topology, wherein the nodes of the decision dimension topology are composed of the features of each dimension, and the edge weights are determined by the dimension correlation.

[0018] Preferably, the specific implementation process of step 111 is as follows: Based on the business attributes of complex decision-making scenarios, multimodal semantic parsing and feature extraction are performed on decision objectives, constraints, and business data to generate a multi-dimensional semantic feature set of decision requirements; specifically, firstly, for decision objective text (such as "construction period ≤ 18 months" and "cost ≤ 500 million yuan" for large-scale engineering projects), a domain-adaptive semantic parsing model is used for processing. This model includes a domain terminology recognition layer, a target type classification layer, and a quantitative indicator extraction layer. The domain terminology recognition layer performs word segmentation and entity annotation on the text using a pre-trained industry dictionary (covering core terms in finance, engineering, transportation, and other fields). The target type classification layer classifies the annotated text based on a convolutional neural network (CNN) and outputs target types such as efficiency, cost, and quality. The quantitative indicator extraction layer uses a bidirectional long short-term memory network (Bidirectional Long Short-Term Memory) to extract the target type. The system uses a BiLSTM (BiMemory Memory) approach to extract numerical and threshold information from the target data, forming a target semantic feature vector. Next, it performs rule-based parsing on constraints (such as "daily construction workers ≤ 300" and "environmental emissions meet standards"), designing a multi-condition adaptation rule base to categorize constraints into hard and flexible constraints. A logistic regression model is used to determine the insurmountability of constraints, generating constraint semantic feature vectors. Finally, it performs feature engineering on business data (such as historical construction progress, material price fluctuations, and traffic flow data). Statistical features (mean, variance, trend slope, etc.) are directly extracted from structured data, while unstructured data (such as construction log text and weather reports) are transformed into quantitative features through a designed text-data mapping model. The target semantic feature vector, constraint semantic feature vector, and business data quantitative features are then fused to generate a multi-dimensional semantic feature set for decision-making requirements. Each element in the feature set corresponds to the semantic attributes and quantitative indicators of a requirement. The element dimensions are set according to the scenario requirements (usually 50-200 dimensions, for example, 80 dimensions for engineering project scenarios).Preferably, the subsequent processing in step 111 is as follows: Redundant features are removed and key features are strengthened on the multi-dimensional semantic feature set of decision requirements to generate a core semantic feature construct for decision requirements. Specifically, a designed two-stage feature optimization mechanism is adopted. In the first stage, redundant features are removed by calculating mutual information entropy (MI), calculating the mutual information value between any two features, and setting a mutual information threshold (the general threshold range is 0.7-0.9, for example, 0.8). When the mutual information value of two features exceeds the threshold, the feature with higher contribution to the decision objective is retained. The contribution is determined by feature importance scoring (calculated based on the random forest algorithm, with a scoring range of 0-1). In the second stage, an attention mechanism is used... The Mechanism strengthens key features and constructs a scenario-adaptive attention weight matrix. The rows of the matrix correspond to the decision target type, the columns correspond to the feature items in the feature set, and the elements at the intersection represent the association weight of the feature with the corresponding target type (the weight range is 0-1, obtained through iterative optimization of training data; for example, in an engineering scenario, the weight of the cost feature with the cost target is set to 0.92). The multidimensional semantic feature set of decision requirements is multiplied with the attention weight matrix to obtain the core semantic feature construct of decision requirements. This construct is presented in vector form, and the value of each element in the vector reflects the importance and semantic relevance of the corresponding feature.

[0019] Preferably, in the specific technical implementation of step 112, based on the core semantic feature construct of decision-making needs, the scenario-based business correlation degree between each feature is calculated to generate a feature correlation degree matrix; specifically, a scenario-adaptive correlation degree calculation model is designed, which combines the Pearson correlation coefficient. The process involves calculating the Pearson correlation coefficient (range -1 to 1) between any two features in the core semantic feature construct, reflecting the degree of linear association between the features. Then, based on the business logic of specific application scenarios, a business association rule base is constructed. For example, in an engineering project scenario, the business association weight between "material price" and "cost budget" is set to 0.85, and the business association weight between "construction progress" and "construction period target" is set to 0.9. The Pearson correlation coefficient and the business association weight are then weighted and summed (the general weight allocation ratio is 6:4 to 7:3, with 7:3 as an example) to obtain the scenario-based business association degree between features (range -1 to 1). Based on the association degree calculation results between all features, a feature association degree matrix is ​​generated. The rows and columns of the matrix correspond to the feature items in the core semantic feature construct, and the elements at the intersection represent the corresponding scenario-based business association degree. For example, the element in the i-th row and j-th column of the matrix represents the degree of business association between the i-th feature and the j-th feature.Preferably, in one scenario, step 112 is specifically implemented by performing hierarchical clustering based on the feature correlation matrix to determine the core dimension, secondary dimension, and risk dimension, thereby generating a decision dimension topology. Specifically, the designed density-association hybrid clustering algorithm is used. First, a clustering density threshold (generally 5-10 features / cluster, 8 in an example) and an association threshold (generally 0.6-0.8, 0.7 in an example) are set. The feature with the highest association from the feature correlation matrix is ​​selected as the initial cluster center. Features whose association with the center feature exceeds the threshold but are not clustered are included in the same cluster until the number of features in the cluster reaches the density threshold or there are no features that meet the conditions. This process is repeated until all features are clustered, resulting in multiple feature clusters. Subsequently, the importance of each feature cluster is evaluated, and a cluster importance evaluation index system is constructed, including three indicators: the sum of the association between the features in the cluster and the decision objective, the contribution of the features in the cluster to the satisfaction of constraints, and the scope of the influence of the features in the cluster in the business process. The weight of each indicator is determined by the Analytic Hierarchy Process (AHP). The Process of Analysis (AHP) is determined (e.g., the weights of the three indicators are set to 0.4, 0.3, and 0.3 respectively). The overall importance score (range 0-10) for each cluster is calculated. Based on the overall importance score, the dimension types are divided: clusters with scores above a set threshold (generally 7-8 points, for example 7.5 points) are core dimensions; clusters with scores in the middle threshold (generally 4-6 points, for example 4.5-6.5 points) are secondary dimensions; and clusters with scores below the middle threshold are risk dimensions. Finally, the features corresponding to each dimension are used as nodes, and the contextual business relevance between features is used as edge weights to construct the decision dimension topology. Core dimension nodes are marked with high priority (e.g., node weight set to 1.0), secondary dimension nodes are marked with medium priority (node ​​weight set to 0.7), and risk dimension nodes are marked with risk priority (node ​​weight set to 0.5). Edge weights directly use the corresponding values ​​in the feature relevance matrix to clearly represent the strength of the relevance within and between dimensions.

[0020] Preferably, the specific implementation process of step 12 includes: constructing a decision accuracy level classification model based on the decision dimension topology and scenario business rules to generate a decision accuracy requirement table for each dimension; specifically, firstly, the dimension attributes in the decision dimension topology are parsed, including dimension type (core / secondary / risk), data type (numerical / range / logical), and business impact weight (based on domain knowledge, the general range for core dimensions is 0.6-0.8, for example, 0.7; for secondary dimensions, 0.3-0.5, for example, 0.4; for risk dimensions, 0.4-0.6, for example, 0.5). For example, in the financial investment scenario, the attributes of the core dimension "asset return rate" are: type = core dimension, data type = numerical, business impact weight = 0.75; then, a decision accuracy level classification model is designed, which includes accuracy... The system consists of a baseline determination layer, a scenario adaptation and adjustment layer, and a precision threshold generation layer. The precision baseline determination layer sets a basic precision baseline based on the data type (for numerical dimensions, the baseline is ≤5% error; for range-type dimensions, it is ≤8% deviation; and for logical dimensions, it is ≥90% accuracy). The scenario adaptation and adjustment layer adjusts the baseline based on the scenario's real-time requirements and business impact weights (for high real-time requirements, the precision baseline is increased by 20%-30%; for high business impact weights, the precision baseline is increased by 10%-20%). The precision threshold generation layer converts the adjusted baseline into specific precision requirements (e.g., for the core dimension "asset return rate," the baseline is ≤3% error, which is adjusted to ≤2.1% error based on high real-time requirements). This generates a decision precision requirement table for each dimension, which contains five core fields: dimension ID, dimension type, data type, business impact weight, and precision requirement. Preferably, the implementation process of step 12 further includes: constructing a risk tolerance hierarchical quantitative model based on the decision accuracy requirement table for each dimension and historical risk data to generate a risk tolerance threshold table for each dimension; specifically, designing a risk tolerance hierarchical quantitative model, which includes a risk type identification layer, a tolerance benchmark setting layer, and a dynamic calibration layer. The risk type identification layer classifies risks into probabilistic risks (such as the probability of an accident), impact risks (such as the amount of loss), and transmission risks (such as the probability of risk chain transmission) based on the attribute characteristics of the risk dimension; the tolerance benchmark setting layer sets a benchmark threshold (conservative scenario probabilistic risk benchmark) according to the scenario risk preference (conservative / neutral / aggressive). The thresholds are as follows: ≤5%, impact risk benchmark ≤10%, and transmission risk benchmark ≤8%; the dynamic calibration layer combines historical risk occurrence data with decision accuracy requirements, and performs calibration through a designed risk-accuracy linkage calibration algorithm (risk tolerance threshold = benchmark threshold × (1 + accuracy requirement improvement ratio)). For example, the accuracy requirement error for the core dimension "cost budget" is ≤2% (30% improvement over the basic benchmark), the corresponding tolerance benchmark for "material price increase risk" is ≤8%, and the calibrated threshold is ≤10.4%; a risk tolerance threshold table for each dimension is generated, which contains six fields: risk dimension ID, risk type, scenario risk preference, benchmark threshold, calibrated threshold, and accuracy linkage coefficient.Preferably, in a scenario, when step 12 is specifically implemented, a constraint association topology model is constructed based on the decision accuracy requirement table and risk tolerance threshold table for each dimension to generate a dimension decision constraint association matrix. Specifically, firstly, constraint parameters are extracted from the two tables, and the decision accuracy requirements are converted into accuracy constraint vectors (the dimensions are consistent with the decision dimension topology), and the risk tolerance thresholds are converted into risk constraint vectors. For example, the accuracy constraint vector for the core dimension "schedule" is "error ≤ 3 days", and the risk constraint vector for the corresponding risk dimension "impact of extreme weather" is "probability of occurrence ≤ 12%". Subsequently, a constraint association topology model is designed, which includes a constraint association strength calculation layer, an association direction determination layer, and a matrix construction layer. The constraint association strength is calculated... The layer calculates the correlation strength (range 0-1, the higher the strength, the stronger the correlation) between any two dimensional constraints using mutual information entropy. For example, the correlation strength between "construction period accuracy requirement" and "extreme weather risk tolerance" is 0.72. The correlation direction determination layer determines the direction of constraint correlation through causal reasoning (e.g., increased accuracy requirement → decreased risk tolerance). The matrix construction layer uses decision dimensions as rows and columns, with rows representing the constraint initiating dimension and columns representing the affected dimension. The elements at the intersection are the combined values ​​of "accuracy requirement - risk tolerance - correlation strength" (e.g., "error ≤ 3 days - probability of occurrence ≤ 12% - 0.72") to generate a dimensional decision constraint correlation matrix. The matrix reflects both the constraint parameters of each dimension and the constraint correlation relationship between dimensions. Preferably, in one scenario, step 12 further includes: performing consistency verification and dynamic optimization on the dimensional decision constraint association matrix to generate the final dimensional decision constraint association matrix; specifically, a consistency verification mechanism is constructed to verify whether there is a conflict between the accuracy requirements and risk tolerance of the same dimension (such as excessively high accuracy requirements causing the risk tolerance to exceed the scenario's tolerance range). If a conflict exists, the parameters are adjusted through a constraint coordination algorithm (such as reducing the accuracy requirements of secondary dimensions by 10%-15% to bring the risk tolerance back to a reasonable range); subsequently, based on the association relationship of the decision dimension topology, the association strength value of the matrix is ​​optimized to ensure that the association influence weight of the core dimension constraint on the secondary dimension and risk dimension is higher than the reverse influence (the association strength of the core dimension on the secondary dimension is multiplied by an enhancement coefficient of 1.2); finally, weak association items with association strength below the threshold (general range 0.1-0.2, set to 0.15 for example) are removed, the core association relationship is retained, and the final dimensional decision constraint association matrix is ​​generated. This matrix clearly presents the constraint parameters of each dimension and the constraint linkage relationship between dimensions, providing an accurate constraint basis for the subsequent construction of the decision risk matrix.

[0021] Optionally, step 13 includes: step 131, constructing a risk correlation quantification matrix, using the risk transmission probability between dimensions as a quantification indicator, calculating the risk correlation score for each dimension to generate a risk correlation score table; step 132, marking high-correlation risk dimension pairs based on the risk correlation score table, and constructing a decision risk matrix; step 133, generating a hierarchical risk-type decision demand topology based on the decision dimension topology, the dimension decision constraint correlation matrix, and the decision risk matrix.

[0022] Preferably, the specific implementation process of step 131 is as follows: Based on the topology of the decision dimension and the correlation matrix of the dimension decision constraints, combined with historical decision risk transmission data, a quantitative matrix of risk correlation between dimensions is constructed. The correlation score is calculated with the risk transmission probability as the core indicator, and a dimension risk correlation score table is generated. Specifically, firstly, historical decision case data of the target scenario within the past five years are collected (the sample size is generally in the range of 1000-5000 groups, and is set to 3000 groups for example). The risk occurrence status (occurrence / non-occurrence) and transmission path information of each dimension in the case are extracted to establish a risk transmission sample library. Each record in the sample library contains four key pieces of information: triggering dimension, triggered dimension, transmission time difference, and constraint condition satisfaction status. Then, a risk transmission probability calculation model is designed. This model includes a feature encoding layer, a transmission path mining layer, and a probability output layer. The feature encoding layer transforms the attribute features (such as dimension type, accuracy requirement, and constraint threshold) of the triggering dimension and the triggered dimension into a 128-dimensional feature vector. The transmission path mining layer uses a graph attention network. The Network (GAT) learns dimensional correlation patterns under different constraints. The probability output layer uses a logistic regression model to output the risk transmission probability (range 0-1) between dimensions. Based on this model, the risk transmission probability between any two dimensions in the decision dimension topology is calculated. For example, in an engineering project scenario, the transmission probability between "material price increase risk" and "cost overrun" is calculated to be 0.85, and the transmission probability between "extreme weather" and "construction delay" is calculated to be 0.72. A risk correlation quantification matrix is ​​constructed using all dimensions in the decision dimension topology as rows and columns. The rows of the matrix represent triggering dimensions, the columns represent triggered dimensions, and the elements at the intersection are the corresponding risk transmission probabilities. This probability value is then mapped to a risk correlation score of 0-10 (the mapping rule is score = probability × 10, rounded to one decimal place), generating a dimensional risk correlation score table. The higher the score, the greater the probability of risk transmission between dimensions.

[0023] Preferably, in the specific technical implementation of step 132, a correlation strength threshold is set based on the dimensional risk correlation score table, high correlation risk dimensional pairs are marked, and an initial decision risk matrix is ​​constructed. Specifically, firstly, the correlation strength threshold is determined according to the scenario risk tolerance level (generally 6-8 points, for example 7 points). Dimensional pairs with correlation scores ≥ the threshold are judged as high correlation risk dimensional pairs. At the same time, medium correlation dimensional pairs with scores below the threshold but above the low correlation threshold (generally 3-4 points, for example 3.5 points) are recorded, and those below the low correlation threshold are weak correlation dimensional pairs. Subsequently, an initial decision risk matrix is ​​constructed. The rows and columns of the matrix are consistent with the risk correlation quantification matrix. Cells corresponding to high correlation risk dimensional pairs are filled with "high correlation - score" (e.g., high correlation - 8.5), medium correlation dimensional pairs are filled with "medium correlation - score" (e.g., medium correlation - 4.2), and weak correlation dimensional pairs are filled with "medium correlation - score". For entries with "weak correlation - score" (e.g., weak correlation - 2.1), to enhance the matrix's adaptability to different scenarios, a dimension attribute correction mechanism is designed. For the correlation score between core dimensions and risk dimensions, the core dimension weight coefficient is multiplied (generally ranging from 1.1 to 1.3, set to 1.2 for example). For instance, the original correlation score between "cost budget" (core dimension) and "material price increase risk" (risk dimension) is 8.5, which is corrected to 10.2, still marked as high correlation. For the correlation score between secondary dimensions, the secondary dimension weight coefficient is multiplied (generally ranging from 0.8 to 0.9, set to 0.85 for example). For instance, the original correlation score between "human resource allocation" and "equipment scheduling" (both secondary dimensions) is 5.0, which is corrected to 4.25, marked as medium correlation. This correction mechanism highlights the importance of the correlation between core dimensions and risk dimensions, forming the final initial decision risk matrix.

[0024] Preferably, the specific implementation process of step 133 is as follows: Based on the decision dimension topology, the dimension decision constraint association matrix, and the initial decision risk matrix, a hierarchical risk-type decision demand topology is generated through a topology fusion algorithm; specifically, firstly, key information is extracted from the dimension decision constraint association matrix. The rows of this matrix represent decision dimensions, the columns represent constraint types (such as precision constraints, threshold constraints, and resource constraints), and the elements at the intersections are the constraint parameters corresponding to that dimension (such as the precision constraint parameter of the core dimension "construction period" being "error ≤ 3 days"). These constraint parameters are used as attribute information to supplement the corresponding nodes in the decision dimension topology, so that each node contains three core attributes: dimension type, associated dimension, and constraint parameter; subsequently, a multi-matrix topology fusion algorithm is designed. This algorithm first transforms the association strength information in the initial decision risk matrix into the edge attributes of the decision dimension topology, that is, the topology connecting two dimensions. The weights of edges on nodes are updated to reflect the corresponding risk correlation scores. Edge weights for highly correlated dimension pairs are multiplied by an enhancement factor of 1.5, while those for weakly correlated dimension pairs are multiplied by a weakening factor of 0.8. Next, the fused topology is layered using a spectral clustering algorithm, dividing core dimension nodes into the first layer, secondary dimension nodes into the second layer, and risk dimension nodes into the third layer, while maintaining the edge connections between layers (corresponding edges between core and risk dimensions, and between secondary and risk dimensions). Finally, the layered topology is structurally optimized by removing edge connections between weakly correlated dimension pairs within a layer (retaining only those with medium or higher correlation), while retaining all edge connections between layers. This forms a layered risk-based decision-making topology that includes a hierarchical dimensional structure, constraint attributes, and risk correlation strength. When presented visually, this topology clearly demonstrates the correlation patterns and risk transmission paths of different dimensional levels.Preferably, in a scenario, when implementing step 133, the hierarchical risk-based decision-making requirement topology is dynamically verified and adjusted to ensure its adaptability to the actual decision-making scenario. Specifically, a verification index system is constructed, including three indicators: topology rationality, risk association accuracy, and constraint matching degree. Topology rationality is evaluated by the dimensional association density within the hierarchy (number of associated edges / total number of possible edges), with a reasonable range of 0.3-0.6 (0.45 for example). Risk association accuracy is verified through historical cases (calculating the match rate between highly associated dimension pairs marked in the topology and the actual transmission dimension pairs in historical cases). Constraint matching degree is checked by verifying the consistency between dimension attributes and constraint parameters. If the conformation fails, adjustments are made. If the rationality of the topology is below the threshold, the correlation strength threshold is readjusted and the hierarchical processing is redone. If the accuracy of risk correlation is below the threshold (general range is 0.7-0.8, set to 0.75 for example), historical sample data is added to retrain the risk transmission probability calculation model, the decision risk matrix is ​​updated, and then fusion is performed again. If the constraint matching degree is not up to standard, the constraint parameter attributes of the dimension nodes are corrected to ensure consistency with the dimension decision constraint correlation matrix. After the adjustment is completed, the final hierarchical risk-type decision requirement topology conformation is output. This conformation retains the hierarchical relationship and constraint requirements of the decision dimensions and clarifies the risk correlation strength between dimensions, providing accurate requirement guidance for subsequent model cascading calls.

[0025] Optionally, step 2 includes: step 21, based on the dimensional attributes of the hierarchical risk-type decision-making demand topology, calling the first major model to process the core dimensional data and generate a basic decision result instruction set; step 22, calling the second major model, combining the basic decision result instruction set, secondary dimensional data and decision risk matrix to perform risk assessment and decision optimization, so as to generate a preliminary refined decision adaptation code table; step 23, using the decision chain pruning operation framework to remove redundant derivation links in the preliminary refined decision adaptation code table, and generating a risk-controllable refined decision intermediate feature code table based on the pruned decision chain and results.

[0026] Optionally, step 21 includes: step 211, parsing the core dimension constraints of the hierarchical risk-type decision-making demand topology and generating core dimension decision instructions; step 212, inputting the core dimension decision instructions into the first large model and performing inference operations to generate a basic decision result instruction cluster, wherein the instruction dimensions of the basic decision result instruction cluster correspond one-to-one with the core dimensions.

[0027] Preferably, the specific implementation process of step 211 is as follows: Based on the hierarchical risk-type decision-making demand topology, the constraint parameters and associated risk information of the core dimensions are structurally analyzed to generate a core dimension constraint feature set; specifically, firstly, the first level (core dimension level) of the hierarchical risk-type decision-making demand topology is traversed, and the attribute information of each core dimension node is extracted, including dimension name, accuracy requirement, constraint threshold, associated risk dimension, and risk association strength. For example, in the engineering project scenario, the attribute information of the core dimension "schedule" is: dimension name = schedule, accuracy requirement = error ≤ 3 days, constraint threshold = total schedule ≤ 18 months, associated risk dimension = extreme weather impact, risk association strength = high association - 7.8; then, a core dimension constraint analysis model is designed, which includes a constraint type classification layer, a parameter quantization extraction layer, and a risk association mapping layer. The constraint type classification layer uses a support vector machine (Support Vector Machine). The Machine Learning (SVM) classifies core dimension constraints into numerical constraints (e.g., cost ≤ 500 million yuan), range constraints (e.g., schedule deviation ± 5%), and logical constraints (e.g., critical path cannot be interrupted). The parameter quantification extraction layer transforms non-numerical constraints into quantified parameters (e.g., the logical constraint "critical path cannot be interrupted" is transformed into "critical path interruption probability = 0"). The risk association mapping layer transforms the association strength between the core dimension and the associated risk dimension into risk weight coefficients (generally ranging from 0.1 to 0.3, set to 0.2 for example), and appends them to the core dimension constraint parameters to generate a core dimension constraint feature set. Each feature set element corresponds to the complete constraint and risk association information of a core dimension.

[0028] Preferably, the subsequent processing in step 211 is as follows: The core dimension constraint feature set is subjected to instruction structure transformation and priority sorting to generate core dimension decision instructions; specifically, firstly, a scenario-adaptive instruction generation rule library is constructed, and corresponding instruction formats are formulated for different types of core dimension constraints. The instruction format for numerical constraints is "dimensional name - target value - accuracy requirement", the instruction format for range constraints is "dimensional name - constraint range - allowable deviation value", and the instruction format for logical constraints is "dimensional name - constraint rule - verification condition". For example, the instruction format for the core dimension "schedule" is "schedule - total duration ≤ 18 months - error ≤ 3 days - associated risk weight 0.2"; subsequently, all core dimensions... The constraint feature set is prioritized according to the intensity of associated risk. Core dimension instructions with higher associated risk intensity have higher priority (the sorting rule is high association > medium association > low association, and under the same intensity, they are sorted according to the strictness of accuracy requirements). For example, "cost budget" (high association risk intensity -8.5) has a higher priority than "schedule" (high association -7.8). Finally, the structured instructions and priority information are combined to generate core dimension decision instructions. The instructions contain five core fields: instruction ID, dimension name, instruction content, priority level, and execution time limit (based on the inference delay setting of the first major model, the general range is 50-100ms, and it is set to 80ms in the example), to ensure that the first major model can be executed efficiently in priority order.

[0029] Preferably, in the specific technical implementation of step 212, the core dimension decision instructions are processed for model input adaptation and then input into the first large model to perform inference operations to generate a draft of the basic decision results. Specifically, the core dimension decision instructions are first converted into an input vector format supported by the first large model. The first large model adopts a lightweight compression architecture such as Mobile ViT or DistilBERT, and the input vector dimension is set to a general range (128-512 dimensions, for example, 256 dimensions). The instruction ID, priority level, and execution time limit are converted into numerical features, and the dimension name and instruction content are converted into word embedding features (generated through a pre-trained domain word vector model). The risk weight coefficients in the core dimension constraint feature set are incorporated into the input vector as bias terms. Then, the inference process of the first large model is started. The architecture of the first large model includes an input adaptation layer, a feature extraction layer, an inference operation layer, and a result output layer. The input adaptation layer normalizes the input vector (normalization range is 0-1), and the feature extraction layer extracts core decision features (Mobile ViT or DistilBERT) through lightweight convolution and attention mechanisms. The inference and computation layer uses either ViT-type architecture or distilled key semantic features (DistilBERT-type architecture) to perform rapid inference based on preset core dimension decision-making algorithms (such as Critical Path Method (CPM) for schedule decision-making and linear programming algorithm for cost decision-making). The result output layer converts the inference results into numerical or logical results corresponding to the core dimension constraints, generating a draft of the basic decision results. For example, the draft result of the core dimension "schedule" is "total schedule 17.8 months, critical path node time nodes: 1 month for preliminary preparation, 12 months for main construction, and 4.8 months for final acceptance".Preferably, the subsequent processing in step 212 includes: performing constraint consistency verification and structured encapsulation of the initial draft of the basic decision results to generate a basic decision result instruction set; specifically, firstly, a constraint consistency verification mechanism is constructed, comparing the initial draft of the basic decision results with the constraint parameters of the core dimension constraint feature set, verifying whether the deviation of numerical results is within the accuracy requirement range (e.g., the deviation of the project duration result ≤ 3 days), and verifying whether logical results meet the constraint rules (e.g., whether the critical path is uninterruptible), with a verification pass rate reaching a general range (95%-98%, for example, 97%) or higher; for results that fail verification, triggering local re-inference of the first major model, adjusting the inference parameters (e.g., the constraint relaxation coefficient of the linear programming algorithm, with a general range of 0.01- The standard value is set to 0.05 (e.g., 0.03 for example) until the result meets the constraint requirements. Then, the validated basic decision results are structured and encapsulated. The result of each core dimension is encapsulated in the format of "Instruction ID-Dimension Name-Decision Result-Constraint Satisfaction-Risk Association Hint". The constraint satisfaction is the degree of agreement between the decision result and the constraint parameters (range 0-1, e.g., 0.98 for example). The risk association hint is the potential impact of the associated risk dimension on the decision result (e.g., "Extreme weather may lead to a delay in the construction period, it is recommended to reserve a 15-day buffer period"). All encapsulated results are sorted according to instruction priority to generate a basic decision result instruction cluster. Its instruction dimension corresponds one-to-one with the core dimension to ensure that the subsequent second major model can be optimized in a targeted manner according to the correlation of the core dimensions.

[0030] In one embodiment, the first large model is characterized by "lightweight and fast" as its core feature. In its basic structural parameters, the number of parameters is ≤5 billion. This parameter refers to the total number of trainable weight parameters in the model, covering all updatable weight parameters in the encoder and decoder of the core architecture such as the Transformer architecture, including attention layers, fully connected layers, and normalization layers. It does not include non-trainable bias parameters or fixed encoding parameters. It is lightweight in the large model domain, far lower than the general large models with hundreds of billions of parameters. The "lightweight" feature is achieved by simplifying the parameter dimensions and merging similar feature dimensions, making it adaptable to mid-range computing hardware such as ordinary server GPUs. At the same time, it adopts lightweight compression architectures such as MobileViT and DistilBERT. These architectures are built through model compression technology. Among them, "MobileViT-like" combines visual Transformer with lightweight convolution to reduce spatial feature redundancy, while "DistilBERT-like" extracts the core capabilities from the large model through knowledge distillation and removes redundant attention heads and network layers. The core role is to retain the core inference capability while controlling the number of parameters, providing architectural support for the "lightweight" feature. Engineering performance parameters determine the core characteristic of "speed". Among them, inference latency ≤100ms refers to the complete time taken for the model to go from receiving input data such as the core dimension decision objective and constraints to outputting the basic decision instructions and other decision results. This includes the total time of data preprocessing, model forward propagation, and result post-processing. In large model inference, this falls into the category of "fast response" and can meet the millisecond-level response requirements of real-time decision-making scenarios such as real-time scheduling of industrial equipment, directly reflecting the "speed" characteristic. The computing power consumption of a single inference is ≤50GFLOPs. GFLOPs is one billion floating-point operations. This parameter refers to the computing power consumption of completing one inference operation. The total number of floating-point operations required for a complete inference process is 50 GFLOPs, which is suitable for low-computing-power hardware such as edge computing devices and entry-level GPUs. It does not rely on high-computing-power clusters, supporting "lightweight and fast" in terms of computing cost and speed. The model storage usage is ≤10GB, which refers to the storage size of the weight file after the model is trained and processed by INT8 quantization, such as the common .bin or .pth format file. The 10GB storage requirement can be directly deployed on ordinary server hard drives or edge device storage modules without the need for dedicated large-capacity storage devices. This is the manifestation of the "lightweight" feature at the deployment level.The functional performance parameters match the "basic decision-making" scenario, where the core dimension decision accuracy is ≥90%. This refers to the degree of matching between the output result and the true optimal solution when the model processes core dimension data of decision-making, such as key indicators in the decision objective and hard requirements in the constraints. For classification tasks, this is the accuracy rate, and for regression tasks, it is the error rate. 90% accuracy can meet the basic decision-making needs of the core dimension without sacrificing speed for excessive pursuit of high accuracy, which is suitable for the "fast processing of basic decisions" scenario. Its suitable scenario is to quickly process core dimension basic decisions. Combining the characteristics of lightweight architecture, low computing power consumption, and fast latency, it can prioritize the processing of core dimension data in decision-making, generate basic decision results, and provide input for subsequent optimization stages, forming the functional positioning of "lightweight and fast processing of basic data".

[0031] In one embodiment, the second major model is characterized by "high-precision complexity." Its fundamental structural parameters determine this core "complexity," with a parameter count ≥ 20 billion. This refers to the total number of trainable weight parameters in the model, encompassing deep encoders / decoders and multimodal fusion layers (if multimodal data is involved) in large-scale pre-trained architectures such as Transformer-XL and GPT-4. This 20 billion parameter count falls within the scope of large-scale models, enabling them to capture complex correlation features in decision data, such as implicit relationships between secondary and risk dimensions, and is the core of "complex data processing capabilities." Support; at the same time, it adopts large-scale pre-trained architectures such as GPT-4 extended architecture and ViT-L high-precision vision architecture. These architectures adopt a deep-level, large attention window pre-training design. Among them, the "GPT-4 extended architecture" improves the ability to understand long text decision data such as historical decision records in business data by increasing the number of attention heads and expanding the length of the context window. The "ViT-L architecture" improves the recognition accuracy of visual decision data such as industrial quality inspection images through large-size image block and multi-level feature fusion. Its core role is to mine complex features in the data to support the needs of "complex decision". The engineering performance parameters match the "complex processing" requirements. Among them, the inference latency is ≤500ms, which refers to the complete time taken for the model to process data across all dimensions, including core dimensions, secondary dimensions, and risk dimensions. Due to the need to process more dimensions of data and perform complex correlation operations, the latency is higher than that of the first-largest model. However, 500ms is still within the acceptable range for practical decision-making scenarios such as enterprise-level strategic decision-making and complex equipment operation and maintenance decisions, balancing the relationship between "complex processing" and "efficiency". The computing power consumption per inference is ≥200GFLOPs, which refers to the total number of floating-point operations when processing data across all dimensions. The computing power requirement of 200GFLOPs can support complex feature extraction such as the calculation of the transmission probability between risk dimensions and multi-dimensional data fusion operations, which is the computing power guarantee for "complex decision-making operations". The model storage occupation is ≥50GB, which refers to the storage size of the model weight file after pre-training. Due to the large number of parameters and the need to retain the representation ability of complex features, the storage requirement is higher than that of the first-largest model. It usually needs to be deployed on a high-performance server storage module, which reflects the deployment characteristics of "large-scale complex models". Functional parameters determine the core characteristic of "high precision," with a full-dimensional decision-making accuracy of ≥95%. This refers to the model's decision-making accuracy when processing full-dimensional decision data including core, secondary, and risk dimensions. 95% accuracy is higher than the first-level model. By using a large number of parameters and a complex architecture, it explores the impact of secondary and risk dimensions on decision-making results, corrects deviations in basic decisions, and meets the precise decision-making needs of complex scenarios. Its suitable scenarios are secondary dimension optimization and risk assessment. Combining high precision and complex processing capabilities, it can optimize the basic decision results of the first-level model by incorporating the refined needs of secondary dimensions such as cost control details and potential risks of risk dimensions such as supply chain risk transmission, thus achieving the functional positioning of "high-precision decision-making in complex scenarios."

[0032] Preferably, in the specific implementation of step 22, a multi-source data fusion model is constructed based on the basic decision result instruction cluster, secondary dimension data, and decision risk matrix to generate a decision fusion feature set. Specifically, the basic decision result instruction cluster is first parsed to extract the decision parameters of the core dimensions (such as the time nodes of the schedule and the allocation ratio of the cost budget), execution logic, and constraint satisfaction. For example, in the engineering project scenario, the core parameters of the basic decision result instruction cluster are "total construction period of 17.8 months, cost budget of 500 million yuan, and critical path construction period of 12 months". Subsequently, the secondary dimension data is structured, including human resource allocation plans, equipment scheduling plans, and material supply cycles. Non-numerical data (such as equipment models and personnel skill levels) are transformed into quantitative features (such as equipment efficiency coefficients and personnel skill scores) through an embedding layer, and numerical data (such as the number of personnel and the number of equipment) are normalized (normalization range 0-). 1) Next, extract the risk correlation information from the decision risk matrix, including highly correlated risk dimension pairs, risk transmission probability, and correlation weights, and transform them into risk feature vectors (with dimensions consistent with the decision fusion feature set). Finally, design a multi-source data fusion model, which includes a feature alignment layer, a weight allocation layer, and a fusion operation layer. The feature alignment layer achieves spatiotemporal alignment of the three types of data through timestamps and dimension identifiers. The weight allocation layer assigns basic weights (general range 0.5-0.6, example set to 0.55) to the core decision parameters, adaptation weights (general range 0.2-0.3, example set to 0.25) to the secondary dimension features, and risk weights (general range 0.15-0.25, example set to 0.2) to the risk feature vectors. The fusion operation layer uses the designed weighted attention fusion algorithm to fuse the three types of features according to their weights, generating a decision fusion feature set. The feature set includes three types of feature items: core decision parameters, secondary dimension adaptation parameters, and risk correlation parameters.Preferably, step 22 includes: constructing a risk correlation inference model based on the decision fusion feature set and the decision risk matrix to generate a risk impact assessment report; specifically, designing a risk correlation inference model, which includes a risk transmission path mining layer, an impact degree quantification layer, and a risk level determination layer, wherein the risk transmission path mining layer uses a graph neural network (Graph Neural Network) to... The Generative Neural Network (GNN) traverses the decision risk matrix, uncovering potential risk transmission paths (such as "material supply delay → equipment idleness → project delay → cost overrun") corresponding to core decision parameters and secondary dimension parameters, and marking the risk transmission probability of each path. The impact quantification layer combines the constraints of secondary dimension data (such as human resource limits and equipment scheduling frequency) to calculate the impact of each risk path on the decision objective (impact degree = risk transmission probability × secondary dimension constraint sensitivity, with a general range of constraint sensitivity of 0.3-0.7, set to 0.5 for example). The risk level determination layer classifies the risk level (high / medium / low) according to the impact degree, with a general threshold range of 0.6 / 0.3, set to 0.6 / 0.3 for example), and generates a risk impact assessment report. The report includes five core contents: risk path, transmission probability, impact degree, risk level, and associated decision parameters.

[0033] Preferably, in one scenario, when step 22 is specifically implemented, an iterative optimization model for decision parameters is constructed based on the decision fusion feature set and the risk impact assessment report to generate an optimized decision scheme. Specifically, the optimization objectives are first clarified, including the achievement of secondary dimension objectives (such as human resource utilization rate ≥85%, equipment idle rate ≤10%) and risk level control (elimination of high-risk paths, influence degree of medium-risk paths ≤0.4). Subsequently, an iterative optimization model for decision parameters is designed. This model includes a constraint construction layer, an objective function generation layer, and an iterative solution layer. The constraint construction layer imposes constraints on core dimensions (such as construction period error ≤3 days), secondary dimensions (such as daily construction personnel ≤300 people), and risk constraints (high-correlation risk dimensions). The probability of linkage (≤0.3) is transformed into a mathematical constraint. The objective function generation layer takes maximizing the achievement of secondary dimension objectives as the core objective and minimizing the degree of risk impact as the secondary objective, and constructs a multi-objective optimization function (objective function = 0.6 × achievement of secondary objective + 0.4 × (1 - degree of risk impact)). The iterative solution layer adopts the designed gradient descent-genetic algorithm hybrid solver, using the core parameters and secondary parameters in the decision fusion feature set as optimization variables, and iteratively optimizes until the objective function converges (the convergence threshold is generally in the range of 0.001-0.01, and is set to 0.005 in the example), generating an optimized decision scheme, which includes the adjusted core parameters, secondary dimension optimization strategies, and risk mitigation measures. Preferably, the processing in step 22 further includes: performing link analysis and structured coding on the optimized decision scheme to generate a preliminary refined decision adaptation code table; specifically, firstly, the optimized decision scheme is analyzed to extract decision link information, including the ID, related dimensions (core / secondary / risk), reasoning logic, input parameters, output results, preceding and subsequent steps of each decision link, for example, "Human Resources Optimization - Core Link ID=GC-021, Related Dimension=Secondary Dimension, Reasoning Logic=Dynamically Allocate Manpower Based on Construction Progress, Input Parameters=Construction Node Duration, Output Results=Increase 50 Steelworkers in the Mid-Term, Preceding Steps=Critical Path Construction Plan, Subsequent Steps=Setting..." The process involves "scheduling optimization"; then, a coding rule base is constructed, using a coding format of "stage ID - dimension type - decision logic - parameter range - risk control level" to standardize the coding of each decision stage. The risk control level is determined based on the risk impact assessment report (high-risk control / medium-risk control / low-risk control); finally, the coded decision stages are sorted according to the link sequence, and fields such as stage association strength, constraint satisfaction (range 0-1, example set to 0.92), and risk control effect (range 0-1, example set to 0.88) are added to generate a preliminary refined decision adaptation code table. Each entry in the code table corresponds to the complete information of a decision stage, which facilitates the subsequent pruning framework to identify redundant stages.

[0034] Optionally, step 23 includes: step 231, constructing a decision chain redundancy judgment rule, using the business contribution of the decision link as the judgment basis, marking redundant decision links, and generating redundant link marking feature codes; step 232, removing redundant links from the preliminary refined decision adaptation code table based on the redundant link marking feature codes, and obtaining the pruned decision chain and results; step 233, generating a risk-controllable refined decision intermediate feature code table based on the pruned decision chain and results. Preferably, the specific implementation process of step 231 is as follows: Based on the preliminary refined decision adaptation code table and decision risk matrix, a multi-dimensional business contribution evaluation model is constructed to generate a quantitative matrix of contribution of decision-making links; specifically, firstly, the preliminary refined decision adaptation code table is parsed to extract the decision-making link information, including link ID, associated dimension type (core / secondary / risk), reasoning logic, output result, associated preceding links and subsequent links. For example, the information of the "traffic light timing adjustment - branch road traffic flow analysis" link in the intelligent transportation scenario is: link ID=JT-003, associated dimension type=secondary dimension, reasoning logic=adjust timing based on the 5-minute average traffic flow of the branch road, output result=extend the green light of the branch road by 10 seconds, preceding link=main road traffic flow analysis, subsequent link=regional traffic flow balance verification; then, a multi-dimensional business contribution evaluation model is designed. This model includes a dimension association weight layer, a logical necessity judgment layer and a result impact calculation layer. The dimension association weight layer allocates basic weights according to the link association dimension type (the general range of core dimension association weight is 0). The weights of the primary and secondary dimensions are 0.6-0.8 (0.7 for example); the secondary dimensions are 0.3-0.5 (0.4 for example); and the risk dimensions are 0.4-0.6 (0.5 for example). The weights are adjusted based on the correlation strength between this dimension and other dimensions in the decision risk matrix (for every 1 point increase in correlation strength, the weight increases by 0.05). The logical necessity determination layer uses causal graph reasoning to analyze whether the step is a necessary prerequisite for subsequent steps (a necessary prerequisite is recorded as a logical contribution of 1, and an unnecessary prerequisite is recorded as below 0.3). The result impact calculation layer compares the decision result deviation between "including this step" and "removing this step" to quantify the result contribution (deviation range 0-1, the larger the deviation, the higher the contribution). The basic weight, logical contribution, and result impact are weighted and summed in a 4:3:3 ratio to obtain the business contribution of each decision step (range 0-1), generating a decision step contribution quantification matrix. The rows of the matrix represent the decision step ID, and the columns represent the contribution components (basic weight, logical contribution, result impact, and comprehensive contribution). The intersections represent the corresponding indicator values.Preferably, in one scenario, step 231 further includes: setting a hierarchical redundancy threshold based on the contribution quantification matrix of the decision-making process, constructing decision chain redundancy judgment rules and marking redundant processes to generate a redundant process marking feature code; specifically, firstly, based on the real-time requirements and risk tolerance of the decision-making scenario, a hierarchical redundancy threshold is set (the general range of the high redundancy threshold is 0.2-0.3, for example 0.25; the general range of the low redundancy threshold is 0.1-0.15, for example 0.12), processes with a comprehensive contribution lower than the low redundancy threshold are judged as absolutely redundant processes, processes between the low and high redundancy thresholds are conditionally redundant processes, and processes higher than the high redundancy threshold are necessary processes; subsequently, a decision chain redundancy judgment rule base is constructed, including absolute redundancy judgment rules, conditional redundancy judgment rules, and necessary process protection rules. The rules for determining absolute redundancy are: "Comprehensive contribution < low redundancy threshold → marked as absolute redundancy"; the rules for determining conditional redundancy are: "Low redundancy threshold ≤ comprehensive contribution < high redundancy threshold + no core dimension association + no high-risk dimension association → marked as conditional redundancy"; and the rules for protecting necessary links are: "Comprehensive contribution ≥ high redundancy threshold ∨ associated core dimension ∨ associated high-risk dimension → marked as necessary link". Based on this rule base, all links in the contribution metric matrix of the decision-making link are judged, and a label code is assigned to each redundant link (absolute redundancy is labeled as "RA-link ID-contribution", and conditional redundancy is labeled as "RC-link ID-contribution"). A redundant link label feature code is generated, which also contains the associated dimension of the link, the IDs of the preceding and subsequent links, and provides link association information for subsequent pruning.

[0035] Preferably, in the specific technical implementation of step 232, based on the association between the redundant link marker feature code and the decision link, an adaptive pruning operation is performed to obtain the pruned decision link and result. Specifically, firstly, a decision link topology graph is constructed, with decision links as nodes and inference dependencies between links as edges, transforming the preliminary refined decision adaptation code table into a visual topology structure, clarifying the input-output dependencies of each link; then, an adaptive pruning algorithm is designed, which includes a link dependency analysis module, a redundancy impact assessment module, and a pruning execution module. The link dependency analysis module directly analyzes whether the subsequent links of an absolutely redundant link can obtain input through other preceding links (if so, it is directly marked as pruningable; if not, the link is retained or a replacement inference path is completed), for example, the subsequent link of "branch road traffic flow analysis" (absolute redundancy) is "regional traffic flow analysis". The "balance verification" module obtains input through "main road traffic flow analysis + regional overall traffic flow statistics," and marks the step as eligible for pruning. The redundancy impact assessment module evaluates whether removing conditionally redundant steps will cause a break in the reasoning of risk-related dimensions (if the reasoning link of a high-risk related dimension is broken after removal, the step is retained). For example, "non-motorized vehicle traffic statistics" (conditional redundancy) is associated with "pedestrian crossing safety risk" (high-risk dimension). If there is no corresponding reasoning step for this risk dimension after removal, the step is retained. The pruning execution module removes steps marked as eligible for pruning in the order of "absolute redundancy first, then conditional redundancy," while adjusting the preorder dependencies of subsequent steps. The effective input information of the removed steps is directly passed to its subsequent steps to ensure the logical coherence of the decision-making link, resulting in the pruned decision-making link and the corresponding decision result.Preferably, in one scenario, the specific implementation process of step 233 is as follows: Logical integrity verification and risk correlation completion are performed on the pruned decision chain and results to generate a risk-controllable refined decision intermediate feature code table; specifically, firstly, a logical integrity verification mechanism is constructed, traversing the pruned decision chain to check whether the input of each link is complete, whether the reasoning logic is coherent, and whether the output result can support the execution of subsequent links. For links with logical breakpoints (such as redundant links that a certain link depends on being removed and having no alternative input), the missing reasoning links are generated by calling the inference model of the correlation dimension through the completion mechanism of the decision chain pruning operation framework. For example, after pruning, the "regional traffic flow balance verification" lacks branch road traffic flow input, so the "branch road traffic flow simplified analysis" link is completed (branch road traffic flow is calculated based on historical data and real-time main road traffic flow); subsequently... The process involves completing the risk correlation, combining it with the decision risk matrix, and checking whether the pruned link covers the reasoning requirements of all highly correlated risk dimension pairs. If there are uncovered highly correlated risk dimension pairs (such as the correlation reasoning between "equipment failure risk" and "traffic light timing" being pruned), the risk transmission reasoning link is supplemented to ensure that the decision result reflects the impact of risk correlation. Finally, the pruned and completed decision link is sorted according to the link order, and the core output results, correlation dimensions, and risk correlation information of each link are extracted. These are then structured and encapsulated according to the scenario-adaptive feature code format (dimension type-link ID-output result-risk weight-logic verification identifier) ​​to generate a risk-controllable refined decision intermediate feature code table. Each entry in this feature code table corresponds to the core information of an effective decision link, facilitating rapid analysis and evaluation of subsequent decision risk iterative verification and optimization architecture.

[0036] Optionally, step 3 includes: Step 31, inputting the risk-controllable refined decision intermediate feature code table into the decision risk iterative verification-optimization architecture, comparing it with the preset risk threshold, identifying risk exceeding links and decision logic gaps, and generating a decision risk-logic verification topology; Step 32, reverse-calling the second major model to re-optimize key parameters for the risk exceeding links marked in the decision risk-logic verification topology, and generating a parameter-optimized decision sub-adaptation code table; Step 33, completing the derivation link for the decision logic gaps marked in the decision risk-logic verification topology through the decision chain pruning operation framework, and generating the final intelligent decision association topology graph based on the parameter-optimized decision sub-adaptation code table and the completed link. Optionally, step 31 includes: step 311, extracting risk indicator data from the risk-controllable refined decision intermediate feature code table, comparing it with the risk threshold in the hierarchical risk-type decision demand topology, identifying risk exceeding the standard link, and generating a risk exceeding the standard link feature code; step 312, verifying the logical coherence of the decision link, marking logical fault nodes, and generating a logical fault node association matrix; step 313, generating a decision risk-logic verification topology based on the risk exceeding the standard link feature code and the logical fault node association matrix.

[0037] Preferably, the specific implementation process of step 311 includes: constructing a risk indicator hierarchical extraction model based on the risk-controllable refined decision intermediate feature code table to generate a full-dimensional risk indicator dataset; specifically, firstly, the risk-controllable refined decision intermediate feature code table is parsed to extract risk-related information for each decision-making stage, including stage ID, associated risk dimension, risk quantification value, risk impact range, and risk transmission path. For example, in a large-scale engineering project scenario, the risk information for the "material procurement - steel price fluctuation" stage is: stage ID = GC-012, associated risk dimension = material price increase risk, risk quantification value = 0.75 (corresponding to a 75% probability of occurrence), risk impact range = cost budget dimension, and risk transmission path = material procurement → cost overrun → project delay; subsequently, a risk indicator hierarchical extraction model is designed, which includes... The system comprises a risk type classification layer, an indicator dimension decomposition layer, and a quantification value standardization layer. The risk type classification layer uses a random forest algorithm to classify risks into probabilistic risks (such as the probability of an accident), impact risks (such as the extent of cost overruns), and transmission risks (such as the probability of risk chain transmission). The indicator dimension decomposition layer breaks down each risk indicator into basic indicators (such as the probability of occurrence), derived indicators (such as the probability of occurrence × the degree of impact), and related indicators (such as the synergistic impact coefficient with other risks). The quantification value standardization layer maps risk quantification values ​​of different dimensions to a unified range of 0-1 (the mapping rule is standardized value = (original value - minimum value) / (maximum value - minimum value)), generating a full-dimensional risk indicator dataset. Each record in the dataset includes a stage ID, risk type, basic indicator value, derived indicator value, related indicator value, and impact dimension.Preferably, in a scenario, the processing of step 311 includes: constructing a dynamic comparison mechanism based on the risk thresholds in the hierarchical risk-type decision-making demand topology, identifying risk exceeding the standard, and generating a feature code for the risk exceeding the standard; specifically, firstly, extracting risk threshold information for each dimension from the hierarchical risk-type decision-making demand topology, including basic thresholds (e.g., risk occurrence probability ≤ 0.3), derived thresholds (e.g., risk derived impact value ≤ 0.4), and associated thresholds (e.g., risk synergistic impact coefficient ≤ 0.2), and assigning threshold weights according to the dimension type (the general range of associated threshold weights for core dimensions is 1.2-1.4, for example 1.3; for secondary dimensions it is 0.9-1.0, for example 0.95; for risk dimensions it is 1.1-1.2, for example 1.15); then constructing a dynamic comparison model, which includes a threshold adaptation layer, a deviation calculation layer, and an exceeding the standard judgment layer. The threshold adaptation layer... The threshold is adjusted according to the type of related dimensions in the decision-making process (e.g., the basic threshold of the core dimension related process = original basic threshold / threshold weight). The deviation calculation layer calculates the deviation between each indicator value in the full-dimensional risk indicator dataset and the corresponding adapted threshold (deviation = (indicator value - adapted threshold) / adapted threshold). The exceedance judgment layer sets the deviation threshold (general range is 0-0.1, for example, 0.05). When the deviation of any indicator is greater than or equal to the deviation threshold, the process is judged as a risk exceedance process. Finally, a feature code is generated for each risk exceedance process. The feature code includes process ID, exceedance indicator type (basic / derived / related), original indicator value, adapted threshold, deviation value, impact dimension, and risk transmission path. For example, "GC-012-basic indicator-0.75-0.23-2.26-cost budget-material procurement→cost overrun→construction delay", forming the risk exceedance process feature code.

[0038] Preferably, in one scenario, the specific technical implementation of step 312 includes constructing a logical causal chain verification model based on the decision link information of the risk-controllable refined decision intermediate feature code table to generate a decision link logical dependency matrix; specifically, firstly, the decision link structure information in the risk-controllable refined decision intermediate feature code table is extracted, including the sequence of links, the mapping relationship between preceding and subsequent links, the reasoning premise and output conclusion of each link. For example, in the intelligent transportation scenario, the decision link is "main road traffic flow collection → traffic flow density calculation → green light cycle adjustment → regional traffic flow balance verification", where the reasoning premise of the "green light cycle adjustment" link is "traffic flow density ≥ threshold", and the output conclusion is "green light extended by 10 seconds"; then, a logical causal chain verification model is designed, which includes a premise-conclusion matching layer, a causal strength calculation layer, and a logical consistency judgment layer. The premise-conclusion matching layer is determined by self- The Natural Language Inference (NLI) model verifies whether the reasoning premises of subsequent steps are consistent with the output conclusions of preceding steps (if consistent, the matching degree is recorded as 1; if inconsistent, it is recorded as 0). The causal strength calculation layer calculates the causal correlation strength between the output of preceding steps and the input of subsequent steps (range 0-1, the higher the strength, the closer the causal relationship) through mutual information entropy. The logical consistency judgment layer combines the matching degree and causal strength to obtain the logical coherence coefficient (logical coherence coefficient = matching degree × causal strength). Based on the logical coherence coefficients of all steps, a decision link logical dependency matrix is ​​generated. The rows of the matrix represent the IDs of preceding steps, the columns represent the IDs of subsequent steps, and the elements at the intersection are the corresponding logical coherence coefficients. Cells with coefficients lower than the logical threshold (general range is 0.3-0.5, set to 0.4 in the example) are marked as "logically weakly related".

[0039] Preferably, in one scenario, the processing of step 312 further includes identifying logical fault nodes based on the decision link logical dependency matrix and constructing a logical fault node association matrix; specifically, firstly, the decision link logical dependency matrix is ​​traversed, and the subsequent links corresponding to "logically weak associations" are determined as logical fault candidate nodes, while initial nodes without clear preceding links and termination nodes without subsequent links are identified (termination nodes that are not the decision endpoint are determined as logical fault candidate nodes); then, a logical fault verification mechanism is constructed, and by backtracking the reasoning premise of the candidate node, it is checked whether there are necessary preceding links not included in the link (necessary preceding links are determined by domain knowledge graph retrieval), if they exist, they are determined as logical fault nodes; for each logical fault node... Logical fault nodes are identified by recording their node ID, fault type (missing premise / mismatched conclusion / broken link), associated preceding stage ID, associated subsequent stage ID, and missing reasoning premise, generating a logical fault node information table. Finally, a logical fault node association matrix is ​​constructed, where the rows and columns of the matrix are decision stage IDs, and the elements at the intersection are "fault - degree of impact" (degree of impact = the reduction in the logical coherence coefficient of the fault node on the subsequent stage, ranging from 0 to 1). Cells without logical fault impact are marked as "no association". For example, the element in the i-th row and j-th column of the matrix (i is the fault node, j is the subsequent stage) is "fault - 0.68", indicating that the logical fault of node i causes the logical coherence coefficient of node j to decrease by 68%.

[0040] Preferably, step 313 is specifically implemented by: generating a decision risk-logic verification topology based on the risk exceeding the standard link feature code and the logical fault node association matrix through a topology fusion algorithm; specifically, firstly, a basic topology structure is constructed, with decision-making links as nodes and normal logical dependencies between links as edges, forming an initial decision link topology; then, the risk information in the risk exceeding the standard link feature code is mapped to the basic topology nodes, with risk exceeding the standard nodes marked in red, and the marking content including the exceeding indicator type and deviation value, while the risk transmission path is added to the topology in the form of dashed edges (dashed edge weight = risk transmission probability); then, the fault information in the logical fault node association matrix is ​​mapped to the basic topology, and the logical fault... Nodes are marked in yellow, with the marking information including the type and degree of impact of the fault. Edges with weak logical connections are marked with dashed lines (the weight of the dashed edge equals the logical coherence coefficient). Finally, a topology optimization algorithm is designed, which includes a node clustering layer and an edge simplification layer. The node clustering layer clusters high-correlation risk-exceeding nodes and logical fault nodes (nodes with a correlation strength ≥ 0.7 are grouped together). The edge simplification layer removes dashed edges with weights lower than the simplification threshold (generally 0.1-0.2, set to 0.15 in the example), retaining the core risk transmission path and logical dependencies, generating a decision risk-logic verification topology configuration. This configuration clearly shows the risk distribution and logical structural defects in the decision-making link, providing accurate positioning for subsequent reverse optimization and logical completion.

[0041] Optionally, step 4 includes: Step 41, collecting data on the target achievement degree, execution efficiency, risk control effect, and decision logic adaptability of the final intelligent decision-making association topology graph, and generating a decision execution effect feedback adaptation code table; Step 42, inputting the decision execution effect feedback adaptation code table into the cascaded model weight-decision chain dual optimization adaptation mechanism, adjusting the calling priority and risk weight allocation of the first and second major models, and updating the pruning rules of the decision chain pruning operation framework; Step 43, updating the decision risk matrix of the hierarchical risk-type decision demand topology based on the risk-related data of the decision execution effect feedback adaptation code table, and storing successful decision cases as adaptation templates in the decision feature code knowledge base to complete the iterative optimization of decision-making capabilities.

[0042] Preferably, the specific implementation process of step 41 includes: based on the final intelligent decision-making association topology and decision-making scenario characteristics, constructing a four-dimensional integrated execution effect evaluation index system to generate an index definition and collection rule table; specifically, firstly, analyzing the decision-making objectives, dimensional attributes, constraint parameters, and risk association information in the final intelligent decision-making association topology, and combining the business rules of specific scenarios such as finance, engineering, and transportation, constructing a four-dimensional evaluation index system. The objective achievement index includes three sub-indicators: core objective completion rate, secondary objective satisfaction rate, and constraint compliance rate (the weight of the core objective completion rate is generally in the range of 0.5-0.7, set to 0.6 for example; the secondary objective satisfaction rate is 0.2-0.3, set to 0.25 for example; and the constraint compliance rate is 0.1-0.2, set to 0.15 for example). The execution efficiency index includes the decision execution time. The system comprises three sub-indicators: resource consumption ratio, process progress rate (each with a weight of 1 / 3); risk control effectiveness indicators: risk occurrence probability deviation, risk impact reduction rate, and risk transmission blocking rate (with weights of 0.4, 0.3, and 0.3 respectively); and decision logic fit indicators: logical coherence score, reasoning link rationality score, and result and scenario fit score (each with a weight of 1 / 3). Subsequently, clear definitions, calculation methods, and collection frequencies are established for each sub-indicator. For example, the core objective completion rate is defined as "the ratio of actual completed value to target value," calculated as "completion rate = actual completed value / target value × 100%," and collected at a frequency of "real-time collection + daily summary." An indicator definition and collection rule table is generated, clarifying the physical meaning, data type, accuracy requirements, and collection sources for each indicator.Preferably, in one scenario, the processing of step 41 includes: deploying multi-source data acquisition nodes based on the indicator definition and acquisition rule table, performing collaborative acquisition and raw data preprocessing to generate a standardized raw dataset; specifically, according to the data sources in the acquisition rule table, three types of acquisition nodes are deployed: terminal acquisition nodes are deployed at decision execution terminals (such as construction equipment, traffic signal controllers) to collect real-time execution data (such as equipment operating status, signal timing execution status); periodic acquisition nodes are deployed on servers to collect summary data (such as the progress of phased target completion, resource consumption statistics) at a set frequency (generally ranging from 5 minutes to 1 hour, for example, 15 minutes); and traceability acquisition nodes are connected to the decision date. The system collects data on the logical reasoning process (such as the execution trajectory of the decision-making link and parameter adjustment records). During the collection process, a timestamp synchronization mechanism is used to ensure the time consistency of all data (timestamp accuracy is at the millisecond level). Subsequently, the collected raw data is preprocessed, including outlier removal (using the 3σ criterion to remove data that deviates from the mean by more than 3 times the standard deviation), missing value imputation (mean is used to imput numerical data, and mode is used to imput logical data), and data format standardization (uniformly converting to the format of "indicator ID-collection time-value-unit") to generate a standardized raw dataset. Each record in the dataset contains indicator ID, indicator name, collection time, raw value, data source, and preprocessing tags. Preferably, in a scenario, when step 41 is specifically implemented, a multi-dimensional data quantification calibration model is constructed based on the standardized original dataset and the quantification rules of the indicator system to generate a quantification evaluation dataset. Specifically, the multi-dimensional data quantification calibration model is designed, which includes an indicator normalization layer, a cross-dimensional calibration layer, and a comprehensive score calculation layer. The indicator normalization layer uses the min-max normalization method to map the original data of different dimensions to the 0-1 interval (the normalization formula is normalized value = (original value - minimum value) / (maximum value - minimum value)). For example, in a construction project, the original value of "core target completion rate" is 92%, and after normalization, it is 0.92. The cross-dimensional calibration layer introduces a scenario adaptation factor to adjust the calibration of each indicator for different scenarios. The calibration coefficient (the calibration coefficient for risk control effectiveness indicators in financial scenarios generally ranges from 1.1 to 1.3, with an example of 1.2; the calibration coefficient for target achievement indicators in engineering scenarios ranges from 1.0 to 1.2, with an example of 1.1) is used to calibrate the normalized indicator values. The calibrated value = normalized value × calibration coefficient. The comprehensive scoring layer calculates the comprehensive score of the four-dimensional core indicators according to the weight ratio of the indicator system (comprehensive score for target achievement = core target completion rate × 0.6 + secondary target satisfaction × 0.25 + constraint compliance rate × 0.15, and the calculation method for the other three dimensions is the same), generating a quantitative evaluation dataset. The dataset includes indicator ID, core indicator name, sub-indicator name, normalized value, calibrated value, and comprehensive score.Preferably, in one scenario, the processing in step 41 includes: performing structured encoding and association mapping on the quantitative evaluation dataset to generate a decision execution effect feedback adaptation code table; specifically, firstly, an encoding rule base is constructed, adopting a four-level encoding structure of "scenario identifier - core indicator type - sub-indicator ID - quantitative level". The scenario identifier uses a 2-letter code (e.g., GC for engineering scenarios, JT for traffic scenarios), the core indicator type uses a 1-digit code (target achievement rate is 1, execution efficiency is 2, risk control effect is 3, decision logic adaptation is 4), the sub-indicator ID uses a 2-digit code (e.g., core target completion rate is 01), and the quantitative level uses a 1-letter code (0.8-1.0 is A, 0.6 is 0.8). -0.8 is B, 0.4-0.6 is C, 0.2-0.4 is D, and 0-0.2 is E. For example, the code for the core target completion rate (calibrated value 0.92) in an engineering scenario is GC-1-01-A. Then, the information such as the comprehensive score, calibrated value, collection time, and data source in the quantitative evaluation dataset is associated and mapped with the code, with each code corresponding to a complete evaluation data record. Finally, the core indicator type and collection time are sorted to generate a decision execution effect feedback adaptation code table. The code table contains eight fields: code, core indicator name, sub-indicator name, calibrated value, comprehensive score, collection time, data source, and quantitative level, to ensure that the subsequent dual optimization adaptation mechanism can quickly parse and call the feedback data.

[0043] Optionally, step 42 includes: step 421, adjusting the calling priority weights of the first and second largest models in the large model cascade-risk-oriented matching architecture based on the indicator deviation values ​​of the decision execution effect feedback adaptation code table, to generate a model priority parameter tuning instruction set; step 422, updating the risk weight allocation ratio in combination with the risk control data of the decision execution effect feedback adaptation code table, to generate a risk weight parameter tuning correlation matrix; step 423, optimizing the pruning rules of the decision chain pruning operation framework according to the model priority parameter tuning instruction set, the risk weight parameter tuning correlation matrix, and the decision logic adaptation data, to complete the double optimization adaptation.

[0044] Preferably, the specific implementation process of step 421 is as follows: Based on the decision execution effect feedback adaptation code table, extract the indicator deviation value and construct a priority weight adjustment model to generate an initial model priority parameter tuning scheme; specifically, firstly, extract the comprehensive score and preset target value of the four-dimensional core indicators from the decision execution effect feedback adaptation code table, and calculate the indicator deviation value (deviation value = (preset target value - actual comprehensive score) / preset target value). For example, the preset target value for target achievement is 0.9, the actual comprehensive score is 0.85, and the corresponding indicator deviation value is 0.055; then, design the priority weight adjustment model, which includes a deviation level classification layer, a scenario adaptation coefficient calculation layer, and a weight adjustment amount derivation layer. The deviation level classification layer classifies the indicator deviation value into slight deviation (general range 0-0.05, set to 0-0 for example). 05) Moderate deviation (0.05-0.15, for example 0.05-0.15) and severe deviation (>0.15, for example >0.15), the scenario adaptation coefficient calculation layer allocates adaptation coefficients according to the scenario type (finance, engineering, transportation, etc.) and the real-time requirements of decision-making (the general range of adaptation coefficients for scenarios with high real-time requirements is 0.8-1.0, for example 0.9; for non-real-time scenarios it is 1.0-1.2, for example 1.1). The weight adjustment amount derivation layer determines the weight adjustment ratio by multiplying the deviation level and the adaptation coefficient (the adjustment ratio for slight deviation is ±5%, for moderate deviation it is ±10%-15%, and for severe deviation it is ±20%-30%), and generates an initial model priority parameter tuning scheme, which includes the current weights, adjustment directions, and adjustment ratios of the first and second largest models.Preferably, in one scenario, the processing step 421 further includes: constraining and dynamically correcting the initial model priority tuning scheme to generate a model priority tuning instruction set; specifically, firstly, a weight constraint rule library is constructed, setting the sum of the weights of the first and second largest models to always be 1, and the weight of a single model must not be lower than a preset lower limit (generally 0.3-0.4, for example 0.35), to avoid a model being excessively idle or overused; then, the initial model priority tuning scheme is verified, and if the adjusted weights exceed the constraint range, they are corrected according to the upper or lower constraint limit, and the weights of the other model are adjusted simultaneously (for example, the current weight of the first largest model is 0.6, the initial...). The initial adjustment ratio is -20%, and the adjusted value is 0.48, which does not exceed the constraint. The weight of the second largest model is adjusted from 0.4 to 0.52. If the current weight of the first largest model is 0.35, the initial adjustment ratio is -10%, and the adjusted value is 0.315, which is lower than the lower limit of 0.35, so it is corrected to 0.35, and the weight of the second largest model is simultaneously corrected to 0.65. Finally, the corrected weight parameters are associated with the scene identifier, effective time, and adjustment basis to generate a model priority parameter tuning instruction cluster. The instruction cluster includes instruction ID, model type (first largest model / second largest model), adjusted weight, effective scene, effective duration (general range is 1-7 days, set to 3 days for example), and indicator deviation basis.Preferably, in the specific technical implementation of step 422, a risk weight iterative update model is constructed based on the risk control data of the decision execution effect feedback adaptation code table to generate an initial risk weight parameter tuning scheme. Specifically, firstly, the three sub-indicator data of risk control effect (risk occurrence probability deviation, risk impact reduction rate, and risk transmission blocking rate) are extracted from the decision execution effect feedback adaptation code table, and the comprehensive effectiveness value of risk control is calculated (comprehensive effectiveness value = 0.4 × risk occurrence probability deviation + 0.3 × risk impact reduction rate + 0.3 × risk transmission blocking rate). For example, if the three sub-indicator values ​​of a certain risk dimension are 0.8, 0.75, and 0.7, the corresponding comprehensive effectiveness value is 0.76. Subsequently, a risk weight iterative update model is designed, which includes an effectiveness level judgment layer, a weight adjustment coefficient calculation layer, and a cross-dimensional collaborative adjustment layer. The stratification layer divides the comprehensive performance value into high performance (general range 0.8-1.0, example set to 0.8-1.0), medium performance (0.6-0.8, example set to 0.6-0.8), and low performance (<0.6, example set to <0.6). The weight adjustment coefficient calculation layer assigns adjustment coefficients to different performance levels (high performance adjustment coefficient is 0.9-1.0, medium performance is 1.0-1.1, and low performance is 1.1-1.3). The cross-dimensional collaborative adjustment layer considers the correlation between different risk dimensions (based on the decision risk matrix). When the weight of a certain risk dimension is adjusted, the weight of the related dimensions is simultaneously adjusted slightly (the adjustment ratio is 30%-50% of the core dimension adjustment ratio), generating an initial risk weight parameter adjustment scheme. The scheme includes the current weight, adjustment coefficient, adjusted weight, and synchronous adjustment information of related dimensions for each risk dimension.

[0045] Preferably, step 422 includes: performing consistency verification and matrix encapsulation on the initial risk weight tuning scheme to generate a risk weight tuning correlation matrix; specifically, firstly, a risk weight consistency verification mechanism is constructed to verify whether the sum of the adjusted weights of all risk dimensions is 1 (allowable error range is ±0.02). If it exceeds the error range, normalization is performed according to the proportion of each dimension's adjustment ratio (normalized weight = weight adjusted for a certain dimension / sum of adjusted weights for all dimensions); subsequently, it is verified whether the adjusted weights match the risk tolerance of the decision-making scenario (in high-risk scenarios, the weights of high-impact risk dimensions must not be lower than the general range of 0.4-). 0.5 (for example, 0.45); for low-risk scenarios, the value should not exceed 0.3-0.4 (for example, 0.35). If there is a mismatch, a second correction is performed. Finally, the corrected weights of each risk dimension are encapsulated in matrix form to generate a risk weight parameter tuning correlation matrix. The rows of the matrix represent the risk dimension ID, the columns represent the decision dimension type (core / minor / risk), and the elements at the intersection are the adjusted weights of the risk dimension under the corresponding decision dimension. For example, the intersection element of "material price increase risk" (risk dimension ID=R-001) and "cost budget" (core dimension) in the matrix is ​​0.52, which means that the weight of the risk dimension in the core dimension decision is 0.52.

[0046] Preferably, in one scenario, step 423 is specifically implemented as follows: Based on the model priority tuning instruction set, risk weight tuning correlation matrix, and decision logic adaptation data, a pruning rule optimization model is constructed to generate an initial pruning rule adjustment scheme; specifically, firstly, decision logic adaptation data in the decision execution effect feedback adaptation code table is extracted, focusing on analyzing decision links with low logical coherence scores and reasoning link rationality scores, and locating potential defects in the pruning rules (such as incorrectly pruning necessary links or failing to prune redundant links); then, a pruning rule optimization model is designed, which includes a rule influence factor calculation layer, a multi-source data fusion layer, and a rule parameter adjustment layer. The rule influence factor calculation layer quantifies the impact of model priority weights and risk weights on the pruning rules (the higher the model priority weight, the higher the pruning threshold). The more lenient the risk weight, the stricter the pruning threshold. The multi-source data fusion layer integrates the weight parameters in the model priority tuning instruction cluster, the weight data in the risk weight tuning correlation matrix, and the decision logic adaptation data in a 3:4:3 ratio to generate the basis for rule adjustment. The rule parameter adjustment layer adjusts the core parameters in the pruning rules (such as the business contribution threshold and the redundancy judgment threshold). For example, when the weight of the second largest model increases and the weight of a certain risk dimension is high, the business contribution threshold of the associated link of that risk dimension is reduced (general range 0.05-0.1, example set as 0.08→0.06) to reduce the probability of such links being pruned incorrectly. The initial pruning rule adjustment plan is generated, which includes the rule ID, the name of the adjusted parameter, the original parameter value, the adjusted parameter value, and the basis for adjustment. Preferably, in a scenario, step 423 includes the following processing steps: performing effect simulation verification and final confirmation on the initial pruning rule adjustment scheme to complete dual optimization adaptation; specifically, the initial pruning rule adjustment scheme is substituted into the decision simulation platform, and simulation is performed in combination with historical decision case data. The pruning accuracy rate (the proportion of correctly pruned redundant links), the logical integrity retention rate (the proportion of decision logic without gaps after pruning), and the reasoning efficiency improvement rate are statistically analyzed during the simulation process. The simulation verification pass rate should reach a general range of 90%-95%, for example, set to above 92%; if the pass rate does not meet the standard, the pruning rule optimization model is backtracked, and the fusion ratio and parameter adjustment range are readjusted until the standard is met; finally, the pruning rule adjustment scheme that meets the standard is associated and stored with the model priority parameter tuning instruction cluster and the risk weight parameter tuning association matrix, clarifying the linkage relationship among the three (e.g., when a certain model weight is adjusted, the corresponding pruning parameters and risk weights take effect simultaneously), completing the dual optimization adaptation of the large model call priority, risk weight allocation, and pruning rules, ensuring that the system can execute according to the optimized parameters and rules in subsequent decision-making processes.

[0047] Preferably, the specific implementation process of step 43 includes: constructing a dynamic update model for the decision risk matrix based on the risk-related data of the decision execution effect feedback adaptation code table to generate an updated decision risk matrix; specifically, firstly, risk-related data is extracted from the decision execution effect feedback adaptation code table, including the actual occurrence probability, impact degree, transmission path deviation, and actual linkage of risk correlation dimensions for each risk dimension. For example, in the financial investment scenario, the actual occurrence probability of the "market interest rate fluctuation" risk dimension is 0.3 (predicted value 0.2), the impact degree is 0.7 (predicted value 0.5), and the actual correlation strength transmitted to "stock asset depreciation" is 0.85 (predicted value 0.7); subsequently, a dynamic update model for the decision risk matrix is ​​designed, which includes a risk parameter calibration layer, a correlation strength correction layer, and... The matrix structure optimization layer and the risk parameter calibration layer employ a designed exponential smoothing algorithm (the smoothing coefficient has a general range of 0.1-0.3, set to 0.2 for example). The calibrated risk parameters are calculated by combining historical predicted values ​​and actual values ​​(calibrated value = actual value × smoothing coefficient + historical predicted value × (1 - smoothing coefficient)). The correlation strength correction layer adjusts the correlation weights between risk dimensions based on the actual linkage situation (the weight is adjusted by ±0.08 for every 0.1 deviation between the actual correlation strength and the predicted value). The matrix structure optimization layer identifies newly added risk dimensions (such as policy change risk) or failure risk dimensions, and dynamically adds or removes rows and columns of the decision risk matrix to generate an updated decision risk matrix. The rows and columns of the matrix still represent the triggering dimension and the triggered dimension, respectively, and the elements at the intersection are the calibrated risk transmission probability and correlation weight.

[0048] Preferably, in one scenario, the processing of step 43 includes: extracting key elements of successful decision-making cases based on the updated decision risk matrix and the final intelligent decision association topology to generate a core feature set of decision-making cases; specifically, firstly, defining the criteria for successful decision-making cases, namely, goal achievement ≥ preset threshold (general range 0.85-0.9, example set to 0.88), risk control effect ≥ preset threshold (general range 0.8-0.85, example set to 0.82), and decision logic fit ≥ preset threshold (general range 0.8-0.9, example set to 0.85), cases that meet these criteria are judged as successful decision-making cases; then, extracting key elements of successful decision-making cases, including decision scenario features (such as... The key elements of a successful smart transportation case include: scenario characteristics = peak-hour traffic management on main roads; core dimension processing strategy = dynamic extension of green light cycle; risk dimension response plan = equipment overload warning + activation of backup lanes; pruning path = elimination of traffic flow on side roads and timing calculation at intersections; and model call combination = rapid generation of basic solutions by the first major model + optimization of risk correlation by the second major model. These key elements are then quantified and structured to generate a core feature set for the decision case. Each feature set contains three core components: scenario feature vector, strategy feature vector, and risk response feature vector. Preferably, in a scenario, when implementing step 43, the core feature set of the decision case is templated and standardized to generate a decision-adaptive template. Specifically, firstly, a decision template encapsulation rule library is constructed, and the template structure is formulated according to the scenario type (engineering, finance, transportation, etc.) and the decision objective type (cost optimization, efficiency improvement, risk control, etc.). The template includes eight core fields: template ID, scenario feature description, core decision strategy, risk response plan, model call parameters, pruning rule parameters, applicable constraints, and effect evaluation data. Subsequently, the core feature set of the decision case is standardized and encoded. The scenario feature description adopts the "industry" standard. The core objectives and key constraints are described in a "core objectives-key constraints" format (e.g., "traffic-flow management-traffic light timing constraints"). Core decision-making strategies and risk response plans are described in structured language (e.g., "core strategy: the green light cycle on main roads is dynamically adjusted according to traffic density, with an adjustment step of 5 seconds"). Model call parameters record the call priority weights and inference parameters of the first and second largest models. Pruning rule parameters record the business contribution threshold and redundancy judgment criteria. Finally, the encoded content is associated with the effect evaluation data (e.g., objective achievement 0.92, risk control effect 0.86) to generate a decision adaptation template, ensuring that the template can be directly called by subsequent similar scenarios.Preferably, the implementation of step 43 includes: constructing an intelligent indexing system for the decision feature code knowledge base, storing decision adaptation templates in the knowledge base, and completing iterative optimization of decision-making capabilities; specifically, firstly, an intelligent indexing system for the decision feature code knowledge base is designed, adopting a dual indexing structure of scene feature hash index + decision target inverted index. The scene feature hash index converts scene feature vectors into hash values ​​(the hash function uses the improved SHA-256 algorithm to retain the uniqueness of the core features of the scene), and the decision target inverted index uses the decision target type as a keyword to associate the corresponding decision adaptation template ID; subsequently, feature codes are generated for the decision adaptation templates, and the feature codes use... The structure is "Scene Hash Value - Target Type Encoding - Template Version Number" (e.g., "0x7F3A-01-V1.0"), where the target type encoding uses a single digit (cost optimization = 1, efficiency improvement = 2, risk control = 3). Finally, the decision-adaptive template is associated with the feature code and dual index and stored in the decision feature code knowledge base. At the same time, the retrieval algorithm parameters of the knowledge base are updated (e.g., the similarity matching threshold is generally in the range of 0.7-0.85, set to 0.8 for example). This ensures that subsequent similar scenarios can quickly retrieve the adapted template through scene features, completing the iterative optimization of decision-making capabilities. This allows the system to directly reuse successful experiences in similar scenarios, shortening the decision-making cycle.

[0049] The above are merely preferred embodiments of the present invention and are not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A hybrid large-scale model cascaded intelligent decision-making method, characterized in that, include: Step 1: Receive the demand data of complex decision-making scenarios, decompose the decision dimensions and define the accuracy and risk tolerance through the decision demand layering-risk modeling architecture, construct the decision risk matrix to mark the risk correlation of the dimensions, and generate a layered risk-type decision demand topology. Step 2: Based on the topological configuration of hierarchical risk-type decision-making needs, a large model cascade-risk-oriented matching architecture is adopted to call the first and second large models to perform cascade operations. Redundant links are eliminated through the decision chain pruning operation framework to generate a risk-controllable refined decision intermediate feature code table. Step 3: Based on the risk-controllable refined decision intermediate feature code table, evaluate the risk threshold adaptability through the decision risk repeated verification-optimization architecture, reverse optimize the excessive links, and complete the logical gaps to generate the final intelligent decision association topology map. Step 4: Collect the execution effect data of the final intelligent decision-making association topology graph, adjust the architecture parameters and pruning rules through the cascaded model weight-decision chain dual optimization adaptation mechanism, update the decision risk matrix and deposit the decision template into the decision feature code knowledge base, and complete the iterative optimization of decision-making capabilities; Step 1 includes: Step 11: Decompose the data requirements of complex decision-making scenarios into dimensions, and divide them into core decision-making dimensions, secondary dimensions, and risk dimensions to generate a decision-making dimension topology. Step 12: Define the decision accuracy requirements and risk tolerance thresholds for each decision dimension to generate a dimension decision constraint correlation matrix; Step 13: Construct a decision risk matrix, mark the risk relationships between each dimension, and generate a hierarchical risk-type decision demand topology based on the decision dimension topology and the dimension decision constraint association matrix, combined with the decision risk matrix. Step 2 includes: Step 21: Based on the dimensional attributes of the hierarchical risk-type decision-making demand topology, call the first major model to process the core dimensional data and generate a basic decision result instruction set; Step 22: Call the second major model, combine the basic decision result instruction set, secondary dimension data and decision risk matrix to perform risk assessment and decision optimization, so as to generate a preliminary refined decision adaptation code table; Step 23: Eliminate redundant derivation steps in the preliminary refined decision adaptation code table through the decision chain pruning operation framework, and generate a risk-controllable refined decision intermediate feature code table based on the pruned decision chain and results. Step 11 includes: Step 111: Perform semantic parsing and feature extraction on the decision target text, constraints, and business data in the requirement data to generate a decision requirement feature profile.

2. The hybrid large-scale model cascaded intelligent decision-making method according to claim 1, characterized in that, Step 11 also includes: Step 112: Based on the business relevance of the decision-making requirement feature configuration, perform dimensional clustering to divide the core dimension, secondary dimension and risk dimension to generate a decision-making dimension topology. The nodes of the decision-making dimension topology are composed of the features of each dimension, and the edge weights are determined by the dimensional relevance.

3. The hybrid large-scale model cascaded intelligent decision-making method according to claim 1, characterized in that, Step 13 includes: Step 131: Construct a risk correlation quantification matrix, using the probability of risk transmission between dimensions as a quantification indicator, calculate the risk correlation score for each dimension, and generate a risk correlation score table. Step 132: Based on the risk association score table, mark the high-association risk dimension pairs and construct the decision risk matrix; Step 133: Based on the decision dimension topology, the dimensional decision constraint correlation matrix, and the decision risk matrix, generate a hierarchical risk-type decision demand topology.

4. The hybrid large-scale model cascaded intelligent decision-making method according to claim 1, characterized in that, Step 21 includes: Step 211: Analyze the core dimension constraints of the hierarchical risk-type decision-making demand topology and generate core dimension decision instructions; Step 212: Input the core dimension decision instructions into the first large model and perform inference operations to generate a basic decision result instruction cluster. The instruction dimensions of the basic decision result instruction cluster correspond one-to-one with the core dimensions.

5. The hybrid large-scale model cascaded intelligent decision-making method according to claim 1, characterized in that, Step 23 includes: Step 231: Construct decision chain redundancy judgment rules, using the business contribution of the decision-making link as the judgment basis, mark redundant decision-making links, and generate redundant link marking feature codes. Step 232: Based on the redundant link marker feature code, remove the redundant links in the preliminary refined decision adaptation code table to obtain the pruned decision link and result; Step 233: Based on the pruned decision-making chain and results, generate a risk-controllable refined decision-making intermediate feature code table.

6. The hybrid large-scale model cascaded intelligent decision-making method according to claim 1, characterized in that, Step 3 includes: Step 31: Input the risk-controllable refined decision intermediate feature code table into the decision risk iterative verification-optimization architecture, compare it with the preset risk threshold, identify the risk exceeding the standard link and the decision logic gap, so as to generate the decision risk-logic verification topology. Step 32: For the risk-exceeding links marked in the decision risk-logic verification topology, reverse call the second major model to re-optimize the key parameters to generate the optimized decision sub-adaptation code table. Step 33: Complete the derivation link of the decision logic gap marked in the decision risk-logic verification topology using the decision chain pruning operation framework. Based on the optimized decision sub-adaptation code table and the completed link, generate the final intelligent decision association topology graph.

7. The hybrid large-scale model cascaded intelligent decision-making method according to claim 6, characterized in that, Step 31 includes: Step 311: Extract risk indicator data from the intermediate feature code table for risk-controllable refined decision-making, compare it with the risk threshold in the topological configuration of hierarchical risk-type decision-making needs, identify the risk exceeding the standard, and generate the feature code of the risk exceeding the standard. Step 312: Verify the logical coherence of the decision-making chain, mark logical fault nodes, and generate a logical fault node association matrix. Step 313: Based on the correlation matrix between the feature code of the risk exceeding the standard link and the logical fault node, generate the decision risk-logic verification topology.

8. The hybrid large-scale model cascaded intelligent decision-making method according to claim 1, characterized in that, Step 4 includes: Step 41: Collect data on the goal achievement, execution efficiency, risk control effect, and decision logic adaptability of the final intelligent decision-making association topology graph, and generate a decision execution effect feedback adaptation code table. Step 42: Input the decision execution effect feedback adaptation code table into the cascade model weight-decision chain dual optimization adaptation mechanism, adjust the calling priority and risk weight allocation of the first and second largest models, and update the pruning rules of the decision chain pruning operation framework. Step 43: Update the decision risk matrix of the hierarchical risk-type decision requirement topology based on the risk-related data of the adaptation code table according to the feedback of decision execution effect, and store successful decision cases as adaptation templates in the decision feature code knowledge base to complete the iterative optimization of decision-making ability.

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