Bidirectional linkage database table and supervision submission form field synchronous construction method

Abstract

The invention relates to the technical field of database management, in particular to a two-way linkage database table and supervision submission form field synchronous construction method which is applied to a supervision data submission scene. According to the scheme, the method comprises the steps that physical structure metadata of a database table and definition metadata of a supervision submission form are obtained, a semantic vector set is formed by combining metadata semantic analysis and semantic coding, and a bidirectional mapping relation graph is constructed through relation weaving; constructing a linkage propagation path according to a field change event, realizing adaptive bidirectional structure adjustment, generating a field synchronous construction scheme, and driving bidirectional mapping relation graph evolution optimization through feedback learning; according to the method, the bidirectional mapping relation graph and self-adaptive bidirectional structure adjustment are creatively combined, and efficient and self-adaptive bidirectional field synchronous construction is realized.

Description

Technical Field

[0001] This invention relates to the field of database management technology, and in particular to a method for synchronously constructing database tables and regulatory reporting form fields in a two-way linkage manner. Background Technology

[0002] In the context of digital transformation in regulatory reporting, the synchronous construction of fields between database tables and regulatory reporting forms is a core data governance step. It must balance data consistency assurance with improved synchronization efficiency, as the accuracy of field mapping and the efficiency of synchronization response directly impact the quality of regulatory data reporting. Traditional field maintenance methods suffer from insufficient semantic parsing and correlation analysis capabilities, and a lack of adaptive adjustment mechanisms, necessitating a highly efficient, adaptive, bidirectional, and interconnected field synchronization construction solution. First, fields in database tables and regulatory reporting forms need to be maintained separately. The lack of effective semantic understanding and mapping mechanisms leads to repetitive and inefficient maintenance work. Second, in complex business scenarios, fields change frequently, and existing technologies lack adaptive bidirectional structural adjustment capabilities, easily causing inconsistencies between database tables and regulatory reporting forms. Furthermore, existing synchronization solutions lack feedback learning and evolutionary optimization mechanisms, failing to continuously improve the accuracy and efficiency of field synchronization.

[0003] For example, Chinese patent CN113672683B discloses a distributed database metadata synchronization method based on SparkSQL. This method constructs a cluster of nodes based on a Gossip network, using Cockroach nodes of the distributed database cluster as internal nodes and SparkSQL Driver nodes as external nodes. The request and response observers of the external nodes classify and filter messages based on the key of the Gossip messages to extract metadata events related to the user database and data tables. Then, through the metadata event handler interface implemented by the Gossip component, the metadata information of the user database and data tables of the internal nodes is synchronized to the SparkSQL data warehouse. This combines the high availability transaction processing capabilities of CockroachDB with the online analytical processing advantages of SparkSQL to solve the metadata synchronization problem between the distributed database and the SparkSQL data warehouse.

[0004] For example, Chinese patent CN114625806B discloses a method for constructing temporal RDF and RDFSchema based on a temporal relational database. This method uses the MariaDB temporal relational database as a foundation, first constructing an element-level temporal RDF and RDFSchema graph model, then performing semantic recognition and classification on the temporal tables in the temporal database, subsequently mapping and constructing a temporal RDFSchema based on the semantic information of the table structure according to preset rules, and finally mapping timestamped tuple information to temporal RDF instances. This preserves the temporal semantics of the temporal relational database, supporting data reuse and sharing in the Semantic Web. The disclosed embodiment relies on preset temporal model definitions and table mapping rules to achieve semantic transformation of temporal data, but the correlation between the synchronization strategy and dynamic field changes is weak, lacking adaptive adjustment capabilities.

[0005] The existing technologies described above all suffer from the problems raised in this background: insufficient semantic understanding depth, static mapping relationships, and weak synchronization strategy associations. Therefore, they struggle to meet the core requirements of field synchronization construction accuracy, response speed, and data consistency in regulatory data reporting scenarios. To address these issues, this application provides a bidirectional, interactive method for synchronizing database tables and regulatory reporting form fields. Summary of the Invention

[0006] The technical problem this invention aims to solve is to address the shortcomings of existing technologies by providing a bidirectional, interconnected method for synchronizing database tables and regulatory reporting form fields. This method identifies business and regulatory semantic features through metadata semantic parsing and transforms these features into multi-dimensional semantic vectors through semantic encoding. Relationship weaving technology, combined with field change history records, is used to construct a bidirectional mapping relationship graph, optimizing the field synchronization path. For field change events, a parallel analysis algorithm determines the impact domain and generates a linked propagation path. If synchronization conflicts exist, a consistency assessment mechanism is triggered to prevent data inconsistency. This method can dynamically update the mapping relationship, improving the adaptability and accuracy of database table and regulatory reporting form field synchronization, providing data governance personnel with a real-time synchronization solution, optimizing maintenance efficiency, and improving data consistency.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for synchronizing database tables and regulatory reporting form fields in a two-way linkage manner, applied in regulatory data reporting scenarios, includes:

[0009] Obtain the physical structure metadata of the database tables and the definition metadata of the regulatory reporting forms. Through metadata semantic parsing, identify business semantic features and regulatory semantic features, and generate a two-way semantic feature set.

[0010] The business semantic features and regulatory semantic features in the bidirectional semantic feature set are transformed into multidimensional semantic vectors through semantic encoding, forming a semantic vector set.

[0011] Based on the semantic vector set and combined with the field change history, a multidimensional association relationship between database table fields and regulatory reporting form fields is constructed using relation weaving technology, and a bidirectional mapping relationship graph is built.

[0012] Obtain field change events, determine the influence domain corresponding to the field change events based on the bidirectional mapping relationship graph, and generate linkage propagation paths through parallel analysis algorithms;

[0013] Based on the aforementioned linkage propagation path, synchronization nodes are determined through consistency assessment, and the updated linkage propagation path is obtained.

[0014] Based on the updated linkage propagation path, a field synchronization construction scheme is generated through adaptive bidirectional structural adjustment.

[0015] The process involves semantic parsing of metadata to identify business semantic features and regulatory semantic features, generating a bidirectional semantic feature set, including:

[0016] Obtain the physical structure metadata of the database table, and extract field names, data types, constraints, and relational dependencies through lexical and syntactic analysis to identify business semantic features;

[0017] Obtain the definition metadata of the regulatory reporting form, and extract the form item identifier, data format, validation rules and business logic through structured parsing to identify regulatory semantic features;

[0018] Based on business semantic features and regulatory semantic features, a semantic matching algorithm is used to perform association mapping and generate a two-way semantic feature set.

[0019] The process of constructing a multidimensional association between database table fields and regulatory reporting form fields using relational weaving technology, and building a bidirectional mapping relationship graph, includes:

[0020] Based on the semantic vectors of each field in the semantic vector set, the semantic similarity between fields is calculated using the cosine similarity algorithm, forming a semantic similarity matrix;

[0021] Based on the field change history, the change frequency and impact range of the fields are extracted. Through statistical analysis algorithms, the correlation strength between fields is calculated, and a dynamic weight relationship is constructed.

[0022] Based on the semantic similarity matrix and dynamic weight relationships, a bidirectional mapping relationship graph is constructed using a graph neural network fusion modeling algorithm.

[0023] The field change events are categorized based on the construction method, including database table mapping changes, manual table structure changes, and SQL query logic changes, among which:

[0024] When the database table mapping is changed to a database table field mapping construction method, the field mapping relationship changes due to the adjustment of the database table structure.

[0025] When the manual table creation structure is changed to the manual table creation method, the database table structure changes are caused by the maintenance of fields in the regulatory reporting form.

[0026] When the SQL query logic is changed to use the SQL query construction method, the field generation logic is changed due to the adjustment of the multi-table join query statement.

[0027] The step of determining the influence domain corresponding to the field change event and generating a linkage propagation path based on the bidirectional mapping relationship graph and using a parallel analysis algorithm includes:

[0028] Based on the field change event, locate the change source node in the bidirectional mapping relationship graph, and identify the influencing node and its corresponding influence level by combining the association strength in the dynamic weight relationship and the graph traversal algorithm.

[0029] Based on the bidirectional mapping relationship graph, the propagation path and its length from the change source node to each affected node are calculated using a parallel analysis algorithm. Combined with the association strength, the influence weight of the propagation path is calculated.

[0030] Based on the influencing nodes, their corresponding influence levels, and the influence weights of the propagation paths, influence domains containing different levels are determined through classification and aggregation. These different levels include core influence levels and secondary influence levels.

[0031] Based on the field change events and their corresponding impact domains, a coordinated propagation path is generated using a differentiated propagation strategy.

[0032] The step of generating a coordinated propagation path based on the field change event and its corresponding impact domain, using a differentiated propagation strategy, includes:

[0033] When the field change event is a database table mapping change, a direct mapping propagation strategy is adopted. Based on the core impact level area in the impact domain, a direct propagation path from the change source node to the target mapping field is generated.

[0034] When the field change event is a manual table structure change, a structure synchronization propagation strategy is adopted to generate a multi-stage propagation path that includes table structure creation, field attribute adjustment and constraint relationship update according to different levels in the influence domain.

[0035] When the field change event is an SQL query logic change, a logic reconstruction propagation strategy is adopted. Based on the areas in the influence domain that are secondary influence levels, a recursive propagation path is generated that includes query statement parsing, multi-table association reconstruction, and field generation logic update.

[0036] Based on the influence weight and influence level of the propagation path, the direct propagation path, multi-stage propagation path, and recursive propagation path are prioritized and optimized to generate a coordinated propagation path.

[0037] The step of determining the synchronization node through consistency evaluation based on the linkage propagation path, and obtaining the updated linkage propagation path, includes:

[0038] Based on the propagation nodes in the aforementioned linkage propagation path, a consistency assessment is conducted to detect the consistency of the data format, data type, and constraints of each propagation node, resulting in a consistency score.

[0039] Based on the consistency score, a conflict identification algorithm is used to mark propagation nodes with consistency scores less than a preset threshold as conflict nodes, and to determine propagation nodes with consistency scores greater than or equal to the preset threshold as synchronization nodes.

[0040] Based on the conflicting node, the propagation branch corresponding to the conflicting node is removed from the linkage propagation path. Based on the synchronization node and the remaining propagation branches in the linkage propagation path, the linkage propagation path is reconstructed to obtain the updated linkage propagation path.

[0041] The consistency assessment involves checking the consistency of data format, data type, and constraints at each propagation node to obtain a consistency score, including:

[0042] Based on the propagation node, determine the physical structure metadata of the corresponding database table and the definition metadata of the regulatory reporting form, and extract the data format, data type and constraints of each field;

[0043] Based on the data type of each field, a type compatibility algorithm is used to compare the data type compatibility between the database table fields and the regulatory reporting form fields, and a type compatibility score is calculated.

[0044] Based on the data format of each field, the degree of matching between data formats is verified by a format consistency algorithm, and a format consistency score is calculated.

[0045] Based on the constraints of each field, the constraint compatibility algorithm is used to detect the compatibility of the field's non-null constraints, uniqueness constraints, and foreign key constraints, and the constraint compatibility score is calculated.

[0046] Based on the type compatibility score, format consistency score, and constraint compatibility score, a consistency score for each propagation node is calculated using a weighted fusion algorithm.

[0047] Based on the updated linkage propagation path, an adaptive bidirectional structural adjustment is used to generate a field synchronization construction scheme. This adaptive bidirectional structural adjustment includes adjustments to the database table structure and the regulatory reporting form structure, wherein:

[0048] Based on the synchronization nodes in the updated linkage propagation path, the corresponding field change events are determined, and the synchronization nodes that make database table structure adjustments and regulatory reporting form structure adjustments are identified through the structural difference analysis algorithm.

[0049] When the field change event corresponding to the synchronization node is a database table mapping change, a mapping synchronization adjustment strategy is adopted. The structure adjustment of the regulatory reporting form is achieved by directly mapping and updating the database table fields to the regulatory reporting form fields.

[0050] When the field change event corresponding to the synchronization node is a manual table structure change, a structure creation and adjustment strategy is adopted. Based on the definition metadata of the fields in the regulatory reporting form, the database table structure is adjusted through database table creation, field attribute setting, and constraint relationship establishment.

[0051] When the field change event corresponding to the synchronization node is a change in SQL query logic, a logic restructuring and adjustment strategy is adopted. Based on the updated query logic, the regulatory reporting form structure is adjusted through query result field parsing, dynamic field mapping, and regulatory reporting form structure update.

[0052] Based on the execution result of the adaptive bidirectional structure adjustment, a field synchronization construction scheme is generated through a synchronization state verification algorithm.

[0053] The field synchronization construction scheme also includes updating the field change history through feedback learning to drive the evolution and optimization of the field synchronization construction scheme, including:

[0054] Based on the execution results of the field synchronization construction scheme, the synchronization success rate and data consistency accuracy are calculated through statistical analysis and comparative verification, and synchronization construction effect data is generated.

[0055] Based on the synchronous construction effect data, an evolutionary trend index is generated through multi-period comparative analysis. Based on the evolutionary trend index, the correlation strength in the dynamic weight relationship is updated through feedback learning to obtain the updated correlation strength.

[0056] Based on the updated association strength, the corresponding semantic similarity matrix is ​​recalculated, the dynamic weight relationship is updated, and the network parameters are retrained through a graph neural network fusion modeling algorithm to obtain the reconstructed bidirectional mapping relationship graph.

[0057] Based on the reconstructed bidirectional mapping relationship graph, a mapping relationship is established for field pairs with a correlation strength greater than a preset correlation strength threshold, and the mapping relationship is removed for field pairs with a correlation strength less than the preset correlation strength threshold, thereby generating an evolved bidirectional mapping relationship graph.

[0058] The evolved bidirectional mapping relationship graph is adaptively optimized using a graph-based evolutionary optimization algorithm, and the optimization stops when the evolutionary trend index meets the preset convergence condition. Attached Figure Description

[0059] Figure 1 This is a flowchart illustrating the steps of the method for synchronously constructing database tables and regulatory reporting form fields in the bidirectional linkage of this invention.

[0060] Figure 2 Flowchart of the steps for constructing a bidirectional mapping relationship graph in this invention;

[0061] Figure 3 This is a flowchart of the steps for generating a linkage propagation path in this invention. Detailed Implementation

[0062] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof.

[0063] The term "and / or" in the following text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0064] This application provides a method for synchronously constructing a data structure, including:

[0065] Obtain metadata information of the source data structure and the target data structure, identify the feature attributes at both ends through semantic analysis, and generate a bidirectional feature set;

[0066] The feature information in the bidirectional feature set is converted into a computable multidimensional vector representation to form a feature vector set;

[0067] Based on the set of feature vectors and combined with historical change records, a multi-dimensional mapping relationship between the source and target ends is constructed through correlation analysis, generating a bidirectional relationship network.

[0068] Detect structural change triggering events, determine the scope of influence of the structural change triggering events through parallel computing based on the bidirectional relationship network, and generate change propagation paths;

[0069] Based on the change propagation path, valid nodes are determined through consistency verification to obtain the optimized propagation path.

[0070] Based on the optimized transmission path, a synchronous construction scheme is generated through adaptive structure updates.

[0071] For example, this embodiment uses a regulatory data reporting scenario as an example to specifically illustrate the method for synchronously constructing the database table and regulatory reporting form fields of the above-mentioned two-way linkage.

[0072] In existing technologies, field synchronization between database tables and regulatory reporting forms typically employs two methods. One method uses static mapping, establishing a one-to-one correspondence between database table fields and regulatory reporting form fields through predefined field mapping rules. This method ensures that basic field mapping functionality can be implemented with low maintenance costs when business operations are relatively stable. However, this method suffers from a lag in responding to field changes, and system synchronization efficiency is primarily affected by the complexity of the mapping rules and the frequency of business changes. This lag impacts the system's synchronization performance in dynamic business environments.

[0073] Method two involves manual field synchronization, where the field mapping between the database tables and regulatory reporting forms is manually identified and maintained. While this method theoretically offers high flexibility, in practice, it requires significant manual intervention, resulting in high maintenance costs. Furthermore, it suffers from low maintenance efficiency and data inconsistencies when business rules change frequently. Therefore, existing technologies often struggle to achieve a perfect balance between improving synchronization accuracy, reducing maintenance costs, and ensuring data consistency. This is particularly true in application scenarios with extremely high data quality requirements, such as financial regulation, where existing field synchronization methods fail to meet the dual demands of rapid response and efficient maintenance.

[0074] To achieve efficient and stable synchronization between database tables and regulatory reporting forms in regulatory data reporting scenarios, and to improve the adaptability of the synchronization system to complex business changes, while also maximizing maintenance efficiency by quickly responding to field changes, this application provides a bidirectional, interactive method for synchronizing database tables and regulatory reporting form fields, based on the aforementioned data structure synchronization construction method. Figure 1 As shown, the execution flow of this method includes:

[0075] S1: Obtain the physical structure metadata of the database tables and the definition metadata of the regulatory reporting forms. Through metadata semantic parsing, identify business semantic features and regulatory semantic features, and generate a two-way semantic feature set.

[0076] In this step, the physical structure metadata of the database tables and the definition metadata of the regulatory reporting forms are first obtained. Then, through metadata semantic parsing technology, business semantic features and regulatory semantic features are identified respectively, generating a bidirectional semantic feature set. Compared to traditional single-field mapping methods, this method achieves bidirectional feature extraction at both the business and regulatory levels through metadata semantic parsing. This approach significantly improves the depth and accuracy of field semantic understanding, while enhancing the system's ability to identify potential relationships between fields. By constructing a complete bidirectional semantic feature set, a reliable feature foundation is provided for subsequent semantic encoding, relationship weaving, and mapping relationship construction, thereby ensuring the accuracy and completeness of field mapping.

[0077] On one hand, when obtaining the physical structure metadata of the database table, firstly, the database management system is connected to access the data dictionary table to obtain the physical structure definition of the database table; then, a lexical analyzer is used to perform word segmentation on the physical structure definition to identify morphemes such as field definitions, type declarations, and constraint statements; next, a syntax tree is constructed through a syntax analyzer, and structured information such as field names, data types, constraints, and relational dependencies are extracted from the syntax tree; finally, the extracted structured information is organized into standardized physical structure metadata to identify business semantic features.

[0078] On the other hand, when obtaining the definition metadata of the regulatory reporting form, firstly, the electronic definition file of the regulatory reporting form is read; then, a structured parser is used to perform hierarchical analysis on the definition file to identify the form structure and field attributes; next, key information such as form item identifiers, data formats, validation rules, and business logic is extracted; finally, the extracted information is converted into standardized definition metadata to identify regulatory semantic features.

[0079] Furthermore, when generating the bidirectional semantic feature set, a feature mapping space is first established to store the business semantic features and the regulatory semantic features; then, the business semantic features and the regulatory semantic features are associated and mapped using a semantic matching algorithm; next, an association index between features is established to form a bidirectional queryable feature structure; finally, a bidirectional semantic feature set containing complete business semantic features and regulatory semantic features is generated.

[0080] S2: Transform the semantic features in the bidirectional semantic feature set into multidimensional semantic vectors through semantic encoding to form a semantic vector set;

[0081] It is understandable that semantic features include business semantic features and regulatory semantic features.

[0082] In this step, the semantic features in the bidirectional semantic feature set are semantically encoded, transforming them into multidimensional semantic vectors to form a standardized semantic vector set. Compared to traditional string matching methods, this approach achieves high-dimensional representation of features through semantic vectorization. This significantly improves the richness and computability of feature representation, while enhancing the system's ability to measure field semantic similarity. By constructing a standardized semantic vector set, a mathematical foundation is provided for subsequent multidimensional association analysis and mapping relationship construction, thereby ensuring the scientific validity and reliability of field mapping.

[0083] On one hand, the business semantic features in the bidirectional semantic feature set are semantically encoded. Specifically, firstly, using the field names in the business semantic features as input, a pre-trained word vector model is used to map the lexical parts of the field names after word segmentation into initial word vectors; then, based on the obtained initial word vectors, a context-aware encoder is used to generate context-enhanced vectors by combining contextual information such as the data type and constraints of the fields; next, the initial word vectors and context-enhanced vectors are weighted and fused through an attention mechanism to obtain a business fusion vector; finally, through dimensionality normalization processing, the fusion vector is transformed into a standardized business semantic vector.

[0084] On the other hand, the regulatory semantic features in the bidirectional semantic feature set are semantically encoded. Specifically, firstly, the form item identifiers in the regulatory semantic features are used as input and converted into basic semantic vectors using a domain-specific semantic encoder; then, rule vectors are constructed based on the data format and verification rules in the regulatory semantic features, and a rule encoding network is used to convert business logic into logical vectors; next, the basic semantic vectors, rule vectors, and logical vectors are fused using a multilayer perceptron to obtain a regulatory fusion vector; finally, the fusion vector is converted into a standardized regulatory semantic vector through vector regularization.

[0085] Furthermore, based on the standardized business semantic vectors and standardized regulatory semantic vectors obtained from the above two aspects, a semantic vector set is formed. Specifically, firstly, a vector index space is established to uniformly store the standardized business semantic vectors and standardized regulatory semantic vectors; then, the two types of semantic vectors in the vector index space are processed through vector alignment technology to ensure their comparability within the same vector index space; next, a vector retrieval structure is constructed based on the aligned vectors to support efficient similarity calculation and nearest neighbor search; finally, all aligned semantic vectors with retrieval structures are organized into a structured vector set to form the semantic vector set.

[0086] S3: Based on the semantic vector set, combined with the field change history record, construct a multi-dimensional association relationship between the database table fields and the regulatory reporting form fields through relationship weaving technology, and construct a two-way mapping relationship graph;

[0087] In this step, based on the semantic vector set, combined with the historical field change record, construct a multi-dimensional association relationship between fields through relationship weaving technology, and finally construct a two-way mapping relationship graph. Compared with the traditional one-to-one static mapping method, this method realizes the construction of multi-dimensional and dynamic association relationships through relationship weaving technology. This method significantly improves the adaptability and accuracy of the mapping relationship, and at the same time enhances the system's ability to analyze the impact of field changes. By constructing a complete two-way mapping relationship graph, it provides a network basis for subsequent impact domain analysis and linkage propagation, thus ensuring the accuracy and traceability of field synchronization.

[0088] S4: Obtain the field change event, and based on the two-way mapping relationship graph, determine the impact domain corresponding to the field change event through a parallel analysis algorithm, and generate a linkage propagation path;

[0089] In this step, for the received field change event, based on the two-way mapping relationship graph, determine the change impact domain through a parallel analysis algorithm and generate the corresponding linkage propagation path. Compared with the traditional serial processing method, this method realizes efficient impact domain identification through a parallel analysis algorithm. This method significantly improves the processing efficiency and accuracy of change impact analysis, and at the same time enhances the system's adaptability to complex change scenarios. By constructing a clear linkage propagation path, it provides a decision-making basis for subsequent consistency evaluation and structural adjustment, thus ensuring the efficiency and accuracy of field synchronization.

[0090] S5: According to the linkage propagation path, determine the synchronization nodes through consistency evaluation to obtain the updated linkage propagation path, and according to the updated linkage propagation path, generate a field synchronization construction plan through adaptive two-way structural adjustment;

[0091] In this step, based on the linkage propagation path, determine the synchronization nodes through consistency evaluation and update the propagation path, and finally generate a field synchronization construction plan through adaptive two-way structural adjustment. Compared with the traditional fixed synchronization strategy, this method realizes intelligent structural synchronization through consistency evaluation and adaptive adjustment. This method significantly improves the reliability and adaptability of the synchronization plan, and at the same time enhances the system's ability to handle different types of changes. By generating a complete field synchronization construction plan, it provides clear operation guidance for actual execution, thus ensuring the structural consistency and data accuracy between the database table and the regulatory reporting form.

[0092] Please refer to Figure 2The figure is a flowchart of the steps for constructing a bidirectional mapping relationship graph provided in an embodiment of this application. The specific steps of S3 are as follows:

[0093] S3.1: Based on the semantic vectors of each field in the semantic vector set, the semantic similarity between fields is calculated using the cosine similarity algorithm to form a semantic similarity matrix;

[0094] In this embodiment, the semantic vector set is used as input, and a semantic similarity matrix is ​​constructed by calculating the semantic similarity between fields. Compared with traditional string matching methods, this method achieves precise quantification of the degree of semantic association between fields through cosine similarity calculation in vector space. This approach significantly improves the accuracy and reliability of similarity calculation, while providing a mathematical foundation for subsequent association construction.

[0095] Specifically, the standardized business semantic vectors of the database table fields and the standardized regulatory semantic vectors of the regulatory reporting form fields are first extracted from the semantic vector set. Since the database table fields and regulatory reporting form fields originate from different semantic parsing processes, the generated semantic vectors may have differences in dimensionality, which can lead to inaccuracies in the cosine similarity calculation results.

[0096] A vector dimension detection algorithm is used to obtain the dimension information of semantic vectors for database table fields and regulatory reporting form fields. When inconsistencies in the dimensions of the two types of semantic vectors are detected, dimension alignment is performed on the extracted semantic vectors to ensure that semantic vectors from fields of different sources have the same dimension specifications. Specifically, when the dimension of the business semantic vector is smaller than that of the regulatory semantic vector, a zero-padding strategy is used to expand the low-dimensional vector to a higher dimension; when the dimension of the business semantic vector is larger than that of the regulatory semantic vector, a truncation or average pooling strategy is used to compress the high-dimensional vector to a lower dimension.

[0097] After dimensional alignment, a vector normalization algorithm is used to unify the magnitude of each semantic vector to a unit length, eliminating the impact of vector magnitude differences on similarity calculation. When the dimension of a semantic vector exceeds a preset high-dimensional threshold, principal component analysis is used to reduce the dimensionality of the high-dimensional semantic vector, improving computational efficiency while preserving the main semantic information.

[0098] Then, based on the processed semantic vectors, the semantic similarity between fields is calculated using the cosine similarity algorithm. Batch matrix operations are employed to calculate the semantic similarity of multiple field pairs in parallel. A similarity threshold filtering mechanism is introduced, setting a similarity threshold based on the business scenario, and only retaining field pairs with a similarity exceeding the threshold.

[0099] Finally, the calculated semantic similarity scores of all field pairs are organized into a two-dimensional matrix to obtain the semantic similarity matrix. The row dimension of the matrix corresponds to the number of fields in the database table, the column dimension corresponds to the number of fields in the regulatory reporting form, and the matrix elements represent the semantic similarity values ​​of the corresponding field pairs. A sparse matrix storage format is used to optimize memory usage, and the matrix is ​​normalized to map the similarity values ​​to a standard numerical range.

[0100] S3.2: Based on the field change history, extract the change frequency and impact range of the fields, calculate the correlation strength between fields through statistical analysis algorithms, and construct dynamic weight relationships;

[0101] In this embodiment, using the field change history as input, dynamic weight relationships between fields are constructed by analyzing the change characteristics of the fields. Compared with static structural analysis methods, this method achieves dynamic quantification of field association relationships by introducing historical change information. This approach significantly improves the timeliness and adaptability of association relationships, while providing a dynamic weight foundation for subsequent graph construction.

[0102] First, based on the field change history, a field change event sequence is extracted using a change event classification algorithm. The execution of this algorithm includes: extracting change record information from the field change history, including basic change data such as change time, change type, change content, and involved fields; analyzing the basic change data using a construction method identification algorithm to identify the construction method type based on the characteristic patterns of the change operation; classifying the field change events into three types: database table mapping changes, manual table structure changes, and SQL query logic changes, generating a field change event sequence containing change time, change type, and construction method type identifiers. For different types of field change events, corresponding importance weight coefficients are assigned, with SQL query logic changes having the highest weight, followed by manual table structure changes, and database table mapping changes having a relatively low weight.

[0103] Then, based on the field change event sequence, change frequency data is generated using a change pattern recognition algorithm. The execution of the change pattern recognition algorithm includes: extracting time series information from the field change event sequence, including time-series characteristic data such as change time points, change intervals, and change type distributions for each field; grouping and statistically analyzing the time-series characteristic data using a time window statistical algorithm to calculate the change frequency of database table mapping, manual table creation structure, and SQL query logic, respectively; and weighting the change frequencies of each type using a weighted frequency calculation algorithm combined with importance weighting coefficients and time decay factors to generate change frequency data containing weighted change frequency and change interval statistical characteristics.

[0104] Next, based on the field change event sequence and system structure information, impact scope data is generated using an impact scope analysis algorithm. The execution of this algorithm includes: extracting information about the fields involved in the changes from the field change event sequence, including the source field, target field, and related fields of the changes; extracting dependency information from the system structure information, including foreign key relationships, index dependencies, field reference relationships, and query relationships in database tables; and performing correlation analysis on the affected objects and structural dependency data using a hierarchical impact propagation algorithm to construct table-level dependency networks, field-level dependency networks, and query-level dependency networks, dividing the impact scope into direct impact layers, indirect impact layers, and potential impact layers, generating impact scope data that includes impact levels, a list of affected fields, and propagation paths.

[0105] Finally, based on the change frequency data and the impact range data, a dynamic weight fusion algorithm is used to generate a dynamic weight relationship. The execution of the dynamic weight fusion algorithm includes: extracting frequency weight information from the change frequency data, including frequency characteristic data such as the weighted change frequency and change type weight distribution of each field pair; extracting range weight information from the impact range data, including range characteristic data such as the impact level, propagation intensity, and dependency depth of each field pair; and performing fusion calculation on the frequency characteristic data and range characteristic data using a construction method-aware multidimensional weight calculation algorithm, setting differentiated fusion strategies according to different construction method types, calculating the correlation strength value between fields, and generating a dynamic weight relationship containing the correlation strength value, construction method type identifier, and weight calculation basis.

[0106] S3.3: Based on the semantic similarity matrix and dynamic weight relationship, a bidirectional mapping relationship graph is constructed using a graph neural network fusion modeling algorithm.

[0107] In this embodiment, a basic graph structure is first generated based on the semantic similarity matrix using a graph structure initialization algorithm. Basic node data is extracted from the semantic similarity matrix, and database table fields and regulatory reporting form fields are treated as different types of nodes to generate a node set. Connection edges between nodes are established based on a similarity threshold, generating a semantic edge set. Attribute features are extracted from the basic field information to generate initial node feature vectors. This results in a basic graph structure containing the node set, semantic edge set, and initial node feature vectors.

[0108] Then, based on the basic graph structure and dynamic weight relationships, an enhanced graph structure is generated using an edge feature fusion algorithm. The association strength in the dynamic weight relationships is used as the weight attribute of each edge in the semantic edge set; a comprehensive feature vector is calculated by combining semantic similarity and association strength, containing multi-dimensional features such as semantic similarity, association strength, time factor, and type compatibility; feature normalization is performed to ensure that the numerical ranges of features of different dimensions are consistent. This results in an enhanced graph structure containing a node set, a semantic edge set with multi-dimensional features, and initial feature vectors for the nodes.

[0109] Next, based on the enhanced graph structure, a trained graph convolutional neural network model and node representation vectors are generated through a graph neural network training algorithm. Specifically, this includes constructing a multi-layer graph convolutional neural network model, adaptively learning the importance weights between nodes through a graph attention mechanism, achieving deep fusion of node features and relationship modeling through multiple rounds of iterative training, and extracting the final representation vectors of nodes from the trained network to obtain the trained graph neural network model and the node representation vectors after deep learning.

[0110] Finally, based on the trained model and node representation vectors, a bidirectional mapping graph is generated using a graph construction algorithm. Potential edge connection probabilities are calculated based on node representation vectors to identify implicit field relationships; similarity scores are used to obtain connection confidence scores between field pairs; and high-confidence edge connections are filtered by setting connection probability thresholds to construct the bidirectional mapping graph. The final result is a graph structure containing bidirectional mapping relationships between database table fields and regulatory reporting form fields.

[0111] Please see Figure 3 Here is a flowchart of the steps for generating a linkage propagation path provided in this application embodiment. The specific steps of S4 are as follows:

[0112] S4.1: Based on the field change event, locate the change source node in the bidirectional mapping relationship graph, and identify the influencing node and its corresponding influence level by combining the association strength in the dynamic weight relationship and the graph traversal algorithm;

[0113] In this embodiment, firstly, field change events are acquired. The information sources for these field change events include database change records, regulatory reporting system operation records, and SQL query definition change records. Based on the construction method, the field change events are categorized into three types: database table mapping changes, manual table structure changes, and SQL query logic changes. Specifically, database table mapping changes occur when the database table field mapping construction method is used, due to adjustments in the database table structure; the corresponding change information is obtained by recording database structure changes. Manual table structure changes occur when the manual table construction method is used, due to maintenance of regulatory reporting form fields; the corresponding change information is obtained by recording reporting form maintenance operations. SQL query logic changes occur when the SQL query construction method is used, due to adjustments in multi-table join query statements; the corresponding change information is obtained by recording query statement modifications.

[0114] Then, based on the type and content of the field change event, the change source node is located in the bidirectional mapping graph. This embodiment uses a change type identification mechanism to convert different types of change events into unified node query conditions, achieving precise location within the graph. For database table mapping changes, the database field node is located in the graph using the table field identifier; for manually created table structure changes, the monitoring field node is located in the graph using the reporting field identifier; and for SQL query logic changes, the derived field node is located in the graph using the query output field identifier.

[0115] Next, based on the graph traversal algorithm, starting from the source node of the change, the influencing nodes are identified. This embodiment employs an improved graph traversal algorithm, which improves traversal efficiency and identification accuracy through a hierarchical traversal control strategy and a dynamic pruning strategy. During the traversal process, the influence degree of each reachable node is evaluated by combining the association strength in the dynamic weight relationship and using an influence degree calculation method. The influence degree calculation method adopts a cumulative influence calculation model, considering the association strength and propagation distance between nodes, to achieve a quantitative assessment of the influence degree.

[0116] Finally, based on the impact assessment results, the impact level is determined using an impact level classification method. This embodiment designs a threshold adjustment strategy that dynamically determines the classification criteria based on the characteristics of the change type and the structural features of the graph, labeling the identified impact nodes with the corresponding impact levels. This threshold adjustment strategy optimizes threshold parameter settings by analyzing historical change data and impact propagation patterns, thereby improving the accuracy of level classification.

[0117] S4.2: Based on the bidirectional mapping relationship graph, the propagation path and its length from the change source node to each affected node are calculated using a parallel analysis algorithm. Combined with the association strength, the influence weight of the propagation path is calculated.

[0118] In this embodiment, firstly, a parallel propagation path search algorithm is employed to calculate the propagation paths from the source node to each influencing node based on the node connection relationships in the bidirectional mapping graph, thus obtaining a set of propagation paths. Specifically, this embodiment designs a multi-threaded propagation path search strategy, grouping the influencing nodes according to node type and graph distribution characteristics, and assigning an independent computing thread to each group to achieve parallel processing of the propagation path search. During the propagation path search process, a hybrid search strategy combining depth-first search and breadth-first search is adopted. By setting an upper limit on the propagation path length and a loop detection mechanism, invalid propagation paths are avoided, improving search efficiency. For node pairs with multiple reachable propagation paths, a propagation path deduplication algorithm is used to identify and retain all different propagation paths, ensuring the completeness of the propagation path analysis, and finally obtaining the deduplicated propagation paths.

[0119] Then, propagation path features are calculated for each identified propagation path. Based on the path structure information in the propagation path set, the propagation path features are calculated. Specifically, this embodiment uses a propagation path length calculation method to count the number of edges on the propagation path based on the edge connection relationships, obtaining the path length as a metric for propagation distance. Simultaneously, based on the node information in the bidirectional mapping graph, a complete node sequence on the propagation path is recorded, including node type, node identifier, and node position information in the propagation path, obtaining propagation path structure information, providing detailed structural information for subsequent propagation path analysis. Using a propagation path complexity evaluation method, based on the propagation path structure information, the diversity of node types and the complexity of connection methods in the propagation path are considered to calculate the structural complexity index of the propagation path.

[0120] Next, combining the correlation strength in the dynamic weight relationship, the propagation path influence weight of each propagation path is calculated. Based on the path length, the propagation path structure information, and the correlation strength, the propagation path influence weight is obtained. Specifically, this embodiment employs a propagation path influence weight aggregation algorithm. By analyzing the correlation strength of each edge on the propagation path, a weighted aggregation function is used to calculate the propagation path influence weight. The weighted aggregation function considers the cumulative and attenuation effects of edge weights, and simulates the gradual weakening of influence during the propagation process by designing a weight attenuation factor. For propagation paths containing different types of nodes, a node type weight adjustment mechanism is used to correct the propagation path influence weight according to the importance of the node type, resulting in the final propagation path influence weight.

[0121] S4.3: Based on the influencing nodes, their corresponding influence levels, and the influence weights of the propagation paths, determine the influence domains containing different levels through classification and aggregation;

[0122] In this embodiment, an influence domain comprising different levels is constructed using a classification and aggregation algorithm based on the influencing nodes, their corresponding influence levels, and the influence weights of the propagation paths. These different levels include a core influence level, a secondary influence level, and a boundary influence level. This method, through multi-level influence domain division, achieves refined management of the influence scope and significantly improves the rationality of influence domain division.

[0123] First, based on the influencing nodes, an influencing node feature table is generated using a feature information collection algorithm. Feature information is extracted from each influencing node, including the influence level, the shortest path length from the influencing node to the change source node, the influence weight of the maximum propagation path, the node type identifier, and the node dependency relationship. An influencing node feature table is then established, where each row corresponds to one influencing node and each column corresponds to one feature attribute. Data cleaning, missing value handling, and outlier detection are performed to obtain a complete influencing node feature table.

[0124] Then, based on the influence level and propagation path influence weight in the influence node feature table, a hierarchical classification rule set is generated through a hierarchical classification rule design algorithm. When the influence level of an influence node exceeds a preset core level threshold and its propagation path influence weight exceeds a preset core weight threshold, the node is classified as a core influence level. When an influence node does not meet the core influence level criteria but its influence level exceeds a preset minimum level threshold, the node is classified as a secondary influence level. When an influence node neither meets the core influence level criteria nor the secondary influence level criteria, but is still within the propagation path coverage area, the node is classified as a boundary influence level. This results in a hierarchical classification rule set containing core influence level determination rules, secondary influence level determination rules, and boundary influence level determination rules.

[0125] Next, based on the hierarchical classification rule set, nodes in the influence node feature table are grouped using a classification aggregation algorithm to generate hierarchical grouping results. Each influence node is assigned a hierarchy according to the judgment conditions in the hierarchical classification rule set; nodes within each hierarchy are sorted in descending order based on the propagation path influence weight values; and a similarity-based aggregation method is used to group nodes with similar propagation characteristics into the same group. Through similarity measurement calculation and aggregation judgment, a hierarchical grouping result containing node allocation at each hierarchy and intra-group ranking is obtained.

[0126] Then, based on the node distribution in the hierarchical grouping results, an optimized hierarchical grouping result is generated using a classification aggregation optimization algorithm. This involves checking for improperly classified nodes, including hierarchical boundary nodes and isolated nodes; re-evaluating hierarchical boundary nodes to determine their final hierarchical affiliation; and re-aggregating isolated nodes to more suitable hierarchical levels to obtain the optimized hierarchical grouping result.

[0127] Finally, based on the hierarchical information and node organization relationships in the optimized hierarchical grouping results, an influence domain determination algorithm is used to generate influence domains containing different levels. The classification and aggregation results are organized into a structured influence domain description, including hierarchical identifiers, node sets, feature statistics, and hierarchical relationship descriptions; a hierarchical description document is generated for each level; a mapping of relationships between levels is established; and a processing strategy identifier is assigned to each level, resulting in influence domains with different levels, including core influence levels, secondary influence levels, and boundary influence levels. In this embodiment, based on the influencing nodes, their corresponding influence levels, and propagation path influence weights, a classification and aggregation technique is used to construct influence domains containing different levels, including core influence levels, secondary influence levels, and boundary influence levels. Compared to a single influence domain division method, this method achieves refined management of the influence scope through multi-level influence domain division. This approach significantly improves the rationality of influence domain division and provides a basis for subsequent differentiated processing.

[0128] S4.4: Based on the field change event and the corresponding impact domain, generate a linkage propagation path through a differentiated propagation strategy.

[0129] In this embodiment, based on the defined influence domain, an appropriate propagation strategy is selected according to the type of field change event to generate the final linkage propagation path. This method adapts to the characteristics of different types of changes through differentiated strategies, significantly improving the targeting and practicality of the propagation path.

[0130] First, for the input field change events, their change category is determined using a type recognition algorithm. A change event type classification model is established, classifying change events into predefined change types based on the event's source object type, operation type identifier, and scope of impact. These change event types include major types such as database table mapping changes, manual table structure changes, and SQL query logic changes, resulting in specific type identifiers for each change event.

[0131] Then, based on the identified change event types, a strategy selection algorithm selects the corresponding differentiated propagation strategy from a pre-configured strategy library. A strategy mapping mechanism is used to establish a one-to-one correspondence between change event types and propagation strategies. For database table mapping changes, a direct mapping propagation strategy is adopted. This involves identifying the mapping relationship of the change source field in the database table, constructing point-to-point propagation paths from the change source node to each target mapping field, calculating the propagation weight of each direct propagation path, and obtaining a direct propagation path description that includes path identifier, start node, end node, and propagation weight.

[0132] For manually created table structure changes, a structure synchronization propagation strategy is adopted. Based on different levels of information within the impact domain, a comprehensive propagation path encompassing multiple processing stages is generated. This includes: a table structure creation stage, where a propagation path for creating the new table structure is generated based on nodes at the core impact level; a field attribute adjustment stage, where a propagation path for synchronously adjusting field attributes is generated based on nodes at the secondary impact level; and a constraint update stage, where a propagation path for updating constraints is generated based on nodes at the boundary impact level. Each processing stage is assigned an execution priority and dependency identifier, resulting in a multi-stage propagation path.

[0133] For changes to SQL query logic, a logic reconstruction propagation strategy is adopted. Based on the node information in the secondary impact hierarchy, a composite propagation path containing query logic reconstruction is generated. This includes query statement parsing processing, performing syntax parsing and semantic analysis on the affected SQL query statements; multi-table join reconstruction processing, reconstructing the join logic and connection conditions between tables; field generation logic update processing, updating the generation logic of calculated fields and derived fields; and recursive analysis algorithms are used to process complex nested queries and subquery structures to obtain the recursive propagation path of logic reconstruction.

[0134] Finally, based on the generated propagation paths, the final linked propagation paths are generated through priority ranking and path optimization algorithms. The propagation paths are comprehensively ranked according to factors such as influence weight, influence level, and path complexity. A multi-factor weighted scoring algorithm is used to calculate the priority score of each propagation path; path optimization processing is performed, including redundant path elimination and loop detection. A path similarity analysis algorithm is used to identify redundant paths with overlapping functions, and a loop detection algorithm from graph theory is used to identify and process possible path loops. The optimized propagation paths are then organized into structured linked propagation paths.

[0135] The specific steps for S5 are as follows:

[0136] S5.1: Based on the propagation nodes in the aforementioned linkage propagation path, a consistency assessment is conducted to check the consistency of the data format, data type, and constraints of each propagation node, and a consistency score is obtained.

[0137] In this embodiment, a comprehensive consistency assessment is performed on each propagation node in the linkage propagation path, and a multi-dimensional detection mechanism is used to ensure data compatibility and structural consistency between nodes.

[0138] First, based on the propagation nodes in the collaborative propagation path, a node metadata set is generated using a metadata extraction algorithm. Using the propagation nodes as input data, a propagation node parsing operation is performed to obtain the basic attribute information of each node based on its identifier and node type. A database metadata query operation is then performed to retrieve the corresponding database table physical structure metadata, including table structure definition, field attribute configuration, and index information, based on the database table identifier of the propagation node. A regulatory metadata query operation is also performed to obtain the regulatory reporting form definition metadata, including field definition specifications, data requirement descriptions, and verification rule configuration, based on the regulatory reporting form identifier of the propagation node. A field attribute extraction operation is then performed to extract the complete attribute description of each field, including data format specifications, data type definitions, and constraint configuration. Finally, a field mapping relationship establishment operation is performed to establish a correspondence between database table fields and regulatory reporting form fields, resulting in a node metadata set containing complete field attributes and mapping relationships.

[0139] Then, based on the data type of each field, a type compatibility score is calculated using a type compatibility algorithm. This involves: performing a data type extraction operation to extract the data type definitions of the corresponding field pairs from the node metadata set, including basic data types, precision parameters, and length limits; performing a type compatibility rule matching operation to find compatibility rules between database table field types and regulatory reporting form field types based on a predefined data type compatibility rule library; performing a type conversion feasibility analysis operation to assess the feasibility and security of converting from database table field types to regulatory reporting form field types; performing a complex type structure parsing operation using a type structure parsing algorithm to recursively analyze the internal structure and components of the type; and performing a type compatibility scoring operation to calculate a type compatibility score for each field pair, resulting in a score reflecting the degree of compatibility between the database table fields and the regulatory reporting form fields.

[0140] Next, based on the data format of each field, a format consistency score is calculated using a format consistency algorithm. This involves: extracting the format pattern definitions for corresponding field pairs from the node metadata set, including the decimal places and thousands separator configuration for numeric formats, the encoding method and case sensitivity for character formats, and the display mode for date formats; performing a format pattern matching operation, using pattern matching techniques from the format consistency algorithm to compare the format specifications of database table fields with those of regulatory reporting forms; assessing the complexity of format conversion and the risk of data loss; verifying the feasibility of format conversion, validating the degree of data format matching between fields and the feasibility of conversion; and finally, calculating a format consistency score for each field pair to obtain a score reflecting the degree of data format matching between the database table fields and the regulatory reporting form fields.

[0141] Then, based on the constraints of each field, a constraint compatibility score is calculated using a constraint compatibility algorithm. This involves: extracting constraint definitions for corresponding field pairs from the node metadata set, including NOT NULL constraints, uniqueness constraints, foreign key constraints, and check constraints; performing NOT NULL constraint compatibility checks to compare the consistency of null value handling strategies and assess the likelihood of constraint conflicts and data migration risks; performing uniqueness constraint compatibility checks to analyze whether the uniqueness requirements of database table fields and regulatory reporting form fields are consistent; performing foreign key constraint compatibility checks to check the existence of related tables and the compatibility of related fields; performing check constraint compatibility checks to compare the logical rules of constraints and assess the consistency of constraint rules; and finally, performing constraint compatibility scoring to calculate a constraint compatibility score for each field pair, resulting in a score reflecting the degree of compatibility between the constraints of the database table fields and the regulatory reporting form fields.

[0142] Finally, based on the type compatibility score, format consistency score, and constraint compatibility score, a weighted fusion algorithm is used to calculate the consistency score for each propagation node. The process involves determining the weights of the scoring dimensions using the analytic hierarchy process (AHP) to establish the weight coefficients for type compatibility, format consistency, and constraint compatibility; calculating the overall consistency score at the field level by weighting and fusing the three-dimensional scores for each field pair using a weighted fusion algorithm; calculating the overall consistency score for each propagation node based on the overall consistency score and the field importance weights; dynamically optimizing the scoring weights based on historical synchronization success rate data and node performance; and standardizing the consistency scores to ensure comparability and usability, resulting in a consistency score that reflects the overall compatibility of each propagation node.

[0143] S5.2: Based on the consistency score, using a conflict identification algorithm, propagation nodes with a consistency score less than a preset threshold are marked as conflict nodes, and propagation nodes with a consistency score greater than or equal to the preset threshold are determined as synchronization nodes.

[0144] In this embodiment, a conflict identification mechanism distinguishes between synchronized nodes and conflicting nodes, providing node classification information for subsequent path optimization. First, based on historical synchronization experience and business fault tolerance requirements, a threshold optimization algorithm determines a preset threshold for the consistency score. Specifically, using historical synchronization operation data and business requirement configuration as input data, historical data statistical analysis is performed. Based on historical synchronization operation records, the distribution relationship between synchronization success rate and consistency score is statistically analyzed, establishing a score-success rate mapping model. Next, ROC curve analysis is performed, using the ROC curve analysis method to plot the synchronization success rate as the true positive rate and the false positive rate as the false positive rate, determining the optimal threshold point and balancing the relationship between synchronization success rate and false positive rate. Then, business fault tolerance quantification is performed, quantifying the business's tolerance for data inconsistency based on business importance level and data quality requirements, establishing a business fault tolerance assessment model. Finally, dynamic threshold adjustment is performed, dynamically adjusting the preset threshold based on the specificities of the current business scenario and real-time performance requirements, adapting to changes in the needs of different business environments, and obtaining the optimal preset threshold that suits the current business environment and historical experience.

[0145] Secondly, based on the preset threshold, the propagation nodes are classified using a conflict identification algorithm to distinguish between conflicting nodes and synchronization nodes. Using the consistency score of each propagation node and the preset threshold determined in the preceding steps as input data, a node traversal operation is performed, traversing all propagation nodes in the linkage propagation path and extracting the consistency score and node identification information for each node. The conflict identification algorithm is then used to compare the consistency score of each node with the preset threshold, and the nodes are classified and labeled based on the comparison results. A conflict node labeling operation is performed; propagation nodes with a consistency score less than the preset threshold are labeled as conflicting nodes, and their conflict identification, conflict discovery time, and conflict severity are recorded. A synchronization node determination operation is performed; propagation nodes with a consistency score greater than or equal to the preset threshold are determined as synchronization nodes, and their synchronization priority, synchronization reliability level, and expected synchronization performance are labeled. This yields a set of node classification results containing the classification information for conflicting and synchronization nodes.

[0146] Furthermore, based on the consistency scores of the conflicting nodes, a conflict type identification algorithm is used to analyze the specific conflict type and severity of the conflicting nodes. Using the conflicting node set and detailed score data obtained in the preceding steps as input data, the primary and secondary conflict dimensions are identified based on the specific values ​​of the conflicting nodes' type compatibility score, format consistency score, and constraint compatibility score. Based on the abnormal patterns in the scores of each dimension, a conflict type identification algorithm is used to determine the conflict type. Based on the deviation of the consistency score from a preset threshold and the scope of conflict impact, a conflict severity assessment model is used to calculate the conflict severity. Based on the conflict type and node metadata information, a root cause analysis algorithm is used to trace the root causes of the conflict, including factors such as data source differences, changes in business rules, and system version incompatibility. Finally, based on the conflict type, severity, and root cause, detailed conflict analysis results are obtained.

[0147] Finally, based on the detailed conflict analysis results, a conflict resolution strategy matching algorithm is used to generate targeted processing suggestions and solutions for each conflict node. Using a pre-built conflict type knowledge base, pattern matching technology is employed to match identified conflict nodes with standard conflict patterns in the knowledge base, identifying similar historical conflict cases. Based on the conflict pattern matching results, a processing strategy retrieval operation is performed to retrieve corresponding processing methods and solutions from the processing strategy library, including data transformation strategies, constraint adjustment schemes, and format standardization methods. For identified complex hybrid conflicts, a composite conflict decomposition operation is performed, using a multi-level conflict decomposition algorithm to decompose the composite conflict into multiple independent single conflicts, and corresponding processing strategies are formulated for each, improving the operability and success rate of conflict resolution. Based on the conflict severity, processing complexity, and business impact scope, a processing priority ranking operation is performed to determine the priority order of processing tasks for each conflict node, optimizing the allocation efficiency of processing resources.

[0148] S5.3: Based on the conflicting node, remove the propagation branch corresponding to the conflicting node from the linkage propagation path, and based on the synchronization node and the remaining propagation branches in the linkage propagation path, reconstruct the linkage propagation path to obtain the updated linkage propagation path;

[0149] In this embodiment, based on the conflict node identification results, a reliable updated linkage propagation path is generated through a path reconstruction mechanism to ensure the successful execution of subsequent synchronization operations. First, based on the conflict nodes and the bidirectional mapping graph, a conflict branch identification operation is performed to obtain a set of propagation branches that need to be removed. A graph traversal algorithm is used to analyze the upstream and downstream dependencies of the conflict nodes in the bidirectional mapping graph, identifying all propagation path branches affected by the conflict nodes, including directly related branches and indirectly dependent branches. Specifically, a depth-first search algorithm is used to start from the conflict nodes and traverse along the related edges in the bidirectional mapping graph, recording all propagation path segments passing through the conflict nodes to form a set of conflict branch identifiers.

[0150] Then, based on the conflict branch identifier set and the connectivity constraints of the linked propagation path, a propagation branch removal operation is performed to obtain the residual path structure after removing the conflict branches. A branch isolation algorithm is employed to accurately locate and remove the corresponding propagation path segments based on the branch information in the conflict branch identifier set, ensuring that the removal operation does not disrupt the overall connectivity and logical integrity of the path. For critical path nodes identified in the conflict branch identifier set, a path rerouting mechanism is used to find alternative connection methods based on alternative paths in the bidirectional mapping graph, maintaining the continuity of the propagation path. Furthermore, the removal information of each branch in the conflict branch identifier set is recorded, including the reason for removal, the scope of impact, and potential recovery schemes, providing a reference for subsequent path optimization.

[0151] Next, based on the synchronized nodes and residual path structure, a path reconstruction operation is performed to obtain candidate updated linkage propagation paths. First, based on the available nodes and connections in the residual path structure, a path reconstruction search space is constructed, forming a set of candidate path nodes and a set of candidate connection edges. Then, the shortest path algorithm is used to recalculate the optimal connection path between synchronized nodes. Using synchronized nodes as the starting and ending points of path reconstruction, the optimal propagation path is searched in the search space formed by the candidate path node set and the candidate connection edge set using Dijkstra's algorithm. Based on the dynamic weight relationships in the bidirectional mapping graph, considering multiple dimensions such as path length, propagation delay, and resource consumption, a preliminary set of candidate propagation paths is generated. Next, the preliminary set of candidate propagation paths undergoes path optimization processing, introducing a path redundancy mechanism to establish backup paths for critical propagation paths. The multidimensional relationships in the bidirectional mapping graph are used to expand the set of candidate propagation paths, improving the system's fault tolerance and stability. Optionally, a path load balancing algorithm is used to distribute propagation tasks among multiple optional paths in the candidate propagation path set. Load optimization is achieved by dynamically adjusting propagation weights, ultimately obtaining the candidate updated linkage propagation paths.

[0152] Finally, based on the candidate updated propagation paths, a path integrity verification operation is performed to obtain the updated propagation paths. A breadth-first search algorithm is used to verify the connectivity of all synchronized nodes. A topological sorting sequence is constructed based on the association strength in the bidirectional mapping graph to check dependencies. Path execution efficiency and resource consumption are evaluated using complexity analysis. Based on the verification results, path pruning and merging techniques are used to optimize and adjust problematic path segments, ultimately outputting the updated propagation paths that have passed integrity verification.

[0153] S5.4: Based on the updated linkage propagation path, a field synchronization construction scheme is generated through adaptive bidirectional structural adjustment;

[0154] In this embodiment, based on the updated linkage propagation path, an adaptive bidirectional structural adjustment mechanism is used to coordinate the execution of database table structure adjustments and regulatory reporting form structure adjustments, ultimately generating a complete field synchronization construction scheme. The adaptive bidirectional structural adjustment mechanism can adopt corresponding adjustment strategies according to different field change event types, ensuring the accuracy and consistency of the bidirectional structural adjustments.

[0155] First, based on the updated propagation path, a structural difference analysis algorithm is used to screen synchronization nodes that require structural adjustments. Specifically, structural feature vectors of database table fields are extracted from the metadata of the synchronization nodes, including attribute information such as field name, data type, length limit, and non-null constraints. Structural feature vectors of regulatory reporting form fields are also extracted from the metadata of the synchronization nodes, including configuration information such as field identifier, data format, validation rules, and required attributes. A feature vector comparison algorithm is used to compare the structural feature vectors of the database table fields and the regulatory reporting form fields one by one, calculating the structural similarity score between each pair of fields, and generating a structural difference evaluation matrix with database table fields as rows and regulatory reporting form fields as columns. Each synchronization node in the propagation path is traversed, and based on the structural difference evaluation matrix, the current structural state of the database table fields and the regulatory reporting form fields is compared to identify synchronization nodes with structural inconsistencies. These structural differences include differences in field existence, attribute configuration, and constraint settings.

[0156] Then, based on different field change event types, corresponding adaptive adjustment strategies are adopted. For database table mapping changes, a mapping synchronization adjustment strategy is used, which achieves structural adjustments to the regulatory reporting form by directly updating the mapping from database table fields to regulatory reporting form fields. Specifically, based on the mapping relationship information in the synchronization node, the field mapping configurations that need to be updated are identified, including field name mapping, data type mapping, and format conversion mapping. A new field mapping configuration is generated through a mapping rule update algorithm to ensure that database table field changes are accurately reflected in the regulatory reporting form. Furthermore, mapping consistency verification is performed, and the correctness and completeness of the mapping configuration are verified through data sample testing.

[0157] For manually created table structure changes, a structure creation adjustment strategy is adopted. Based on the metadata definition of the fields in the regulatory reporting form, the database table structure is adjusted through database table creation, field attribute setting, and constraint relationship establishment. In a preferred embodiment, based on the standardized requirements of the regulatory reporting form fields, the corresponding database table structure definition is generated, including table name determination, field definition, primary key setting, and index creation. A structure creation script generation algorithm is used to automatically generate the SQL script for database table creation, ensuring the standardization and integrity of the table structure. Optionally, a structure optimization suggestion mechanism is introduced to provide table structure optimization suggestions based on performance optimization and storage efficiency considerations.

[0158] For changes to SQL query logic, a logic restructuring strategy is adopted. Based on the updated query logic, the structure of the regulatory reporting form is adjusted through query result field parsing, dynamic field mapping, and updating the regulatory reporting form structure. Specifically, the modified SQL query statement undergoes syntax parsing and semantic analysis to extract the field structure information of the query results, including field names, data types, and calculation logic. A dynamic mapping algorithm is used to establish a mapping relationship between query result fields and regulatory reporting form fields, supporting the mapping processing of complex calculated fields and aggregate fields. Furthermore, the structure definition of the regulatory reporting form is updated according to the mapping relationship to ensure that the form can correctly receive and process query result data.

[0159] Finally, based on the execution results of the adaptive bidirectional structural adjustment, a field synchronization construction scheme is generated through a synchronization state verification algorithm. Specifically, a synchronization state evaluation model is established based on the execution status of the structural adjustment. The construction of the synchronization state evaluation model includes: extracting execution state data of the synchronization operation from the execution results of the structural adjustment, including execution status information such as adjustment success rate, execution time, and error information; extracting structural state data from the adjusted database tables and regulatory reporting forms, including structural information such as field mapping relationships, data type matching degree, and constraint consistency; and comprehensively analyzing the execution state data and structural state data through a multi-dimensional verification algorithm to calculate the success rate of the synchronization operation, data consistency, and performance indicators, generating a synchronization state evaluation result. Based on the synchronization state evaluation result, a quality inspection is performed through a multi-dimensional verification mechanism, including structural integrity verification, data compatibility verification, and performance efficiency verification, to ensure that the synchronization construction scheme meets the preset quality standards and reliability requirements. Based on the verified synchronization state evaluation result, a field synchronization construction scheme including field mapping configuration, synchronization rule definition, and exception handling strategy is generated. Optionally, a synchronous build report can be generated, which records in detail the adjustment process, execution results, and potential risks, providing a reference for subsequent maintenance and optimization.

[0160] S5.5: Update the field change history through feedback learning to drive the evolution and optimization of the field synchronous construction scheme;

[0161] In this embodiment, based on the execution effect of the field synchronization construction scheme, a feedback learning mechanism is used to continuously optimize the bidirectional mapping relationship graph, thereby achieving adaptive evolution of the field synchronization construction scheme. This mechanism continuously improves the accuracy and efficiency of synchronization construction through multi-cycle effect evaluation and relationship adjustment, forming a self-optimizing closed-loop system.

[0162] Specifically, firstly, based on the execution results of the field synchronization construction scheme, statistical analysis and comparative verification are used to calculate the synchronization success rate and data consistency accuracy, generating synchronization construction effect data. In one implementation, a synchronization effect evaluation model is established to quantitatively evaluate the execution quality of synchronization construction from multiple dimensions. For calculating the synchronization success rate, the ratio of the number of fields successfully synchronized to the total number of synchronized fields is calculated, while the distribution of failure reasons and failure types is recorded. For calculating the data consistency accuracy, a data sampling verification mechanism is used to compare the data content consistency between database table fields before and after synchronization and regulatory reporting form fields, calculating data matching degree and error rate. Furthermore, performance indicators during the synchronization execution process are collected, including execution time, resource consumption, and concurrent processing capabilities, forming a comprehensive synchronization construction effect dataset.

[0163] Then, based on the synchronization effect data, evolutionary trend indicators are generated through multi-period comparative analysis. Specifically, a time series analysis model is established to perform trend analysis on the effect data of multiple consecutive synchronization periods, identifying the changing trend of synchronization success rate, the degree of improvement in data consistency, and the optimization direction of performance indicators. In a preferred embodiment, a sliding window algorithm is used to calculate the rate of change of effect indicators within different time windows, and a trend fitting algorithm is used to predict the future evolutionary direction. Optionally, an anomaly detection mechanism is introduced to identify abnormal fluctuations and abrupt changes in the effect data, analyze the causes of anomalies, and formulate corresponding optimization strategies.

[0164] Next, based on the evolutionary trend indicators, the correlation strength in the dynamic weight relationship is updated through feedback learning to obtain the updated correlation strength. In one implementation, a feedback learning algorithm is established to adjust the correlation strength weights between fields based on the correlation analysis between the synchronization construction effect and the correlation strength. For field pairs with a high synchronization success rate, their correlation strength is enhanced to reflect their stable and reliable mapping relationship; for field pairs with a high synchronization failure rate, their correlation strength is reduced to avoid the continued impact of erroneous mapping. Specifically, a gradient descent algorithm is used to optimize the correlation strength parameters, with the synchronization construction effect as the objective function, and precise optimization of the correlation strength is achieved through iterative adjustment. Furthermore, a regularization mechanism is introduced to prevent overfitting and ensure the generalization ability and stability of the correlation strength adjustment.

[0165] Then, based on the updated association strength, the corresponding semantic similarity matrix is ​​recalculated, and the dynamic weight relationship is updated. Specifically, based on the adjusted association strength weights, cosine similarity calculation is re-performed to generate an updated semantic similarity matrix. Matrix normalization ensures a reasonable distribution and comparability of similarity values. Simultaneously, the weight coefficients in the dynamic weight relationship are updated to reflect the latest field association patterns and importance distribution.

[0166] Next, the network parameters are retrained using a graph neural network fusion modeling algorithm to obtain the reconstructed bidirectional mapping graph. In a preferred embodiment, the graph neural network model is retrained based on the updated semantic similarity matrix and dynamic weight relationships. An incremental learning algorithm is employed to incorporate new feedback information while maintaining existing knowledge, avoiding catastrophic forgetting. The network weights and bias parameters are adjusted using a backpropagation algorithm to optimize the graph's representational ability and prediction accuracy. Optionally, an ensemble learning method can be used, combining multiple graph neural network models with different structures to improve the robustness and accuracy of the graph construction.

[0167] Furthermore, based on the reconstructed bidirectional mapping graph, mapping relationships are established for field pairs with association strength greater than a preset association strength threshold, and mapping relationships are removed for field pairs with association strength less than the preset association strength threshold, generating an evolved bidirectional mapping graph. Specifically, a dynamic association strength threshold is set, which is adaptively adjusted based on historical evolution effects and current business needs. Through a threshold filtering mechanism, high-quality mapping relationships are retained, while low-reliability mapping relationships are removed, ensuring the simplicity and effectiveness of the graph. In one implementation, a threshold optimization algorithm is used to dynamically adjust the association strength threshold based on the graph's connectivity and coverage indicators, balancing the graph's completeness and accuracy.

[0168] Finally, an evolutionary optimization algorithm based on graph structure is used to adaptively optimize the evolved bidirectional mapping graph. Optimization stops when the evolutionary trend index meets a preset convergence condition. In one implementation, a graph structure optimization model is established, and the structural layout and connection patterns of the graph are optimized based on the graph's topological characteristics and node importance. A community detection algorithm is used to identify functional modules and associated clusters in the graph, and modular optimization improves the graph's organization and interpretability. Specifically, convergence conditions are set, including a stability threshold for the evolutionary trend index, a limit on the number of optimization iterations, and a lower limit for the improvement magnitude. When the improvement magnitude of the effect in multiple consecutive optimization cycles is less than the preset threshold, or the evolutionary trend index reaches a stable state, the optimization process is considered to have converged, and further evolutionary optimization operations are stopped.

[0169] Optionally, an evolutionary history record mechanism can be established to record in detail the parameter adjustments, effect changes, and decision-making basis for each evolutionary optimization, forming an evolutionary knowledge base. Through evolutionary pattern analysis, effective optimization strategies and adjustment patterns can be identified, providing experience guidance for subsequent evolutionary optimizations. Furthermore, a multi-objective optimization mechanism can be introduced to find the optimal balance point among multiple objectives such as synchronization success rate, data consistency, and execution efficiency, achieving comprehensive performance optimization.

[0170] Through the above-mentioned systematic feedback learning and evolution optimization implementation scheme, the continuous improvement of the bidirectional mapping relationship graph and the adaptive optimization of the field synchronization construction scheme have been achieved, which significantly improves the system's learning ability and adaptability, and provides intelligent technical support for long-term data governance in regulatory data reporting scenarios.

[0171] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0172] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0173] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0174] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0175] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for synchronously constructing database tables and regulatory reporting form fields through bidirectional linkage, characterized in that: The method includes: Obtain the physical structure metadata of the database tables and the definition metadata of the regulatory reporting forms. Through metadata semantic parsing, identify business semantic features and regulatory semantic features, and generate a two-way semantic feature set. The business semantic features and regulatory semantic features in the bidirectional semantic feature set are transformed into multidimensional semantic vectors through semantic encoding, forming a semantic vector set. Based on the semantic vector set and combined with the field change history, a multidimensional association relationship between database table fields and regulatory reporting form fields is constructed using relation weaving technology, and a bidirectional mapping relationship graph is built. Obtain field change events, determine the influence domain corresponding to the field change events based on the bidirectional mapping relationship graph, and generate linkage propagation paths through parallel analysis algorithms; Based on the aforementioned linkage propagation path, synchronization nodes are determined through consistency assessment to obtain the updated linkage propagation path. Based on the updated linkage propagation path, a field synchronization construction scheme is generated through adaptive bidirectional structural adjustment.

2. The method for synchronously constructing database tables and regulatory reporting form fields in a two-way linkage manner according to claim 1, characterized in that, The process involves semantic parsing of metadata to identify business semantic features and regulatory semantic features, generating a bidirectional semantic feature set, including: Obtain the physical structure metadata of the database table, and extract field names, data types, constraints, and relational dependencies through lexical and syntactic analysis to identify business semantic features; Obtain the definition metadata of the regulatory reporting form, and extract the form item identifier, data format, validation rules and business logic through structured parsing to identify regulatory semantic features; Based on business semantic features and regulatory semantic features, a semantic matching algorithm is used to perform association mapping and generate a two-way semantic feature set.

3. The method for synchronously constructing database tables and regulatory reporting form fields in a two-way linkage manner according to claim 1, characterized in that, The process of constructing a multidimensional association between database table fields and regulatory reporting form fields using relational weaving technology, and building a bidirectional mapping relationship graph, includes: Based on the semantic vectors of each field in the semantic vector set, the semantic similarity between fields is calculated using the cosine similarity algorithm, forming a semantic similarity matrix; Based on the field change history, the change frequency and impact range of the fields are extracted. Through statistical analysis algorithms, the correlation strength between fields is calculated, and a dynamic weight relationship is constructed. Based on the semantic similarity matrix and dynamic weight relationships, a bidirectional mapping relationship graph is constructed using a graph neural network fusion modeling algorithm.

4. The method for synchronously constructing database tables and regulatory reporting form fields in a two-way linkage manner according to claim 3, characterized in that, The field change events are categorized based on the construction method, including database table mapping changes, manual table structure changes, and SQL query logic changes, among which: When the database table mapping is changed to a database table field mapping construction method, the field mapping relationship changes due to the adjustment of the database table structure. When the manual table creation structure is changed to the manual table creation method, the database table structure changes are caused by the maintenance of fields in the regulatory reporting form. When the SQL query logic is changed to use the SQL query construction method, the field generation logic is changed due to the adjustment of the multi-table join query statement.

5. The method for synchronously constructing database tables and regulatory reporting form fields in a two-way linkage manner according to claim 4, characterized in that, The step of determining the influence domain corresponding to the field change event and generating a linkage propagation path based on the bidirectional mapping relationship graph and using a parallel analysis algorithm includes: Based on the field change event, locate the change source node in the bidirectional mapping relationship graph, and identify the influencing node and its corresponding influence level by combining the association strength in the dynamic weight relationship and the graph traversal algorithm. Based on the bidirectional mapping relationship graph, the propagation path and its length from the change source node to each affected node are calculated using a parallel analysis algorithm. Combined with the association strength, the influence weight of the propagation path is calculated. Based on the influencing nodes, their corresponding influence levels, and the influence weights of the propagation paths, influence domains containing different levels are determined through classification and aggregation. These different levels include core influence levels and secondary influence levels. Based on the field change events and their corresponding impact domains, a coordinated propagation path is generated using a differentiated propagation strategy.

6. The method for synchronously constructing database tables and regulatory reporting form fields in a two-way linkage manner according to claim 5, characterized in that, The step of generating a coordinated propagation path based on the field change event and its corresponding impact domain, using a differentiated propagation strategy, includes: When the field change event is a database table mapping change, a direct mapping propagation strategy is adopted. Based on the core impact level area in the impact domain, a direct propagation path from the change source node to the target mapping field is generated. When the field change event is a manual table structure change, a structure synchronization propagation strategy is adopted to generate a multi-stage propagation path that includes table structure creation, field attribute adjustment and constraint relationship update according to different levels in the influence domain. When the field change event is a change in SQL query logic, a logic reconstruction propagation strategy is adopted. Based on the areas in the influence domain that are secondary influence levels, a recursive propagation path is generated that includes query statement parsing, multi-table association reconstruction, and field generation logic update. Based on the influence weight and influence level of the propagation path, the direct propagation path, multi-stage propagation path, and recursive propagation path are prioritized and optimized to generate a coordinated propagation path.

7. The method for synchronously constructing database tables and regulatory reporting form fields in a two-way linkage manner according to claim 6, characterized in that, The step of determining the synchronization node through consistency evaluation based on the linkage propagation path, and obtaining the updated linkage propagation path, includes: Based on the propagation nodes in the aforementioned linkage propagation path, a consistency assessment is conducted to detect the consistency of the data format, data type, and constraints of each propagation node, resulting in a consistency score. Based on the consistency score, a conflict identification algorithm is used to mark propagation nodes with consistency scores less than a preset threshold as conflict nodes, and to determine propagation nodes with consistency scores greater than or equal to the preset threshold as synchronization nodes. Based on the conflicting node, the propagation branch corresponding to the conflicting node is removed from the linkage propagation path. Based on the synchronization node and the remaining propagation branches in the linkage propagation path, the linkage propagation path is reconstructed to obtain the updated linkage propagation path.

8. The method for synchronously constructing database tables and regulatory reporting form fields in a two-way linkage manner according to claim 7, characterized in that, The consistency assessment involves checking the consistency of data format, data type, and constraints at each propagation node to obtain a consistency score, including: Based on the propagation node, determine the physical structure metadata of the corresponding database table and the definition metadata of the regulatory reporting form, and extract the data format, data type and constraints of each field; Based on the data type of each field, a type compatibility algorithm is used to compare the data type compatibility between the database table fields and the regulatory reporting form fields, and a type compatibility score is calculated. Based on the data format of each field, the degree of matching between data formats is verified by a format consistency algorithm, and a format consistency score is calculated. Based on the constraints of each field, the constraint compatibility algorithm is used to detect the compatibility of the field's non-null constraints, uniqueness constraints, and foreign key constraints, and the constraint compatibility score is calculated. Based on the type compatibility score, format consistency score, and constraint compatibility score, a consistency score for each propagation node is calculated using a weighted fusion algorithm.

9. The method for synchronously constructing database tables and regulatory reporting form fields in a two-way linkage manner according to claim 7, characterized in that, Based on the updated linkage propagation path, an adaptive bidirectional structural adjustment is used to generate a field synchronization construction scheme. This adaptive bidirectional structural adjustment includes adjustments to the database table structure and the regulatory reporting form structure, wherein: Based on the synchronization nodes in the updated linkage propagation path, the corresponding field change events are determined, and the synchronization nodes that make database table structure adjustments and regulatory reporting form structure adjustments are identified through the structural difference analysis algorithm. When the field change event corresponding to the synchronization node is a database table mapping change, a mapping synchronization adjustment strategy is adopted. The structure adjustment of the regulatory reporting form is achieved by directly mapping and updating the database table fields to the regulatory reporting form fields. When the field change event corresponding to the synchronization node is a manual table structure change, a structure creation and adjustment strategy is adopted. Based on the definition metadata of the fields in the regulatory reporting form, the database table structure is adjusted through database table creation, field attribute setting, and constraint relationship establishment. When the field change event corresponding to the synchronization node is a change in SQL query logic, a logic restructuring and adjustment strategy is adopted. Based on the updated query logic, the regulatory reporting form structure is adjusted through query result field parsing, dynamic field mapping, and regulatory reporting form structure update. Based on the execution result of the adaptive bidirectional structure adjustment, a field synchronization construction scheme is generated through a synchronization state verification algorithm.

10. The method for synchronously constructing database tables and regulatory reporting form fields in a two-way linkage manner according to claim 9, characterized in that, The field synchronization construction scheme also includes updating the field change history through feedback learning to drive the evolution and optimization of the field synchronization construction scheme, including: Based on the execution results of the field synchronization construction scheme, the synchronization success rate and data consistency accuracy are calculated through statistical analysis and comparative verification, and synchronization construction effect data is generated. Based on the synchronous construction effect data, an evolutionary trend index is generated through multi-period comparative analysis. Based on the evolutionary trend index, the correlation strength in the dynamic weight relationship is updated through feedback learning to obtain the updated correlation strength. Based on the updated association strength, the corresponding semantic similarity matrix is ​​recalculated, the dynamic weight relationship is updated, and the network parameters are retrained through a graph neural network fusion modeling algorithm to obtain the reconstructed bidirectional mapping relationship graph. Based on the reconstructed bidirectional mapping relationship graph, a mapping relationship is established for field pairs with a correlation strength greater than a preset correlation strength threshold, and the mapping relationship is removed for field pairs with a correlation strength less than the preset correlation strength threshold, thereby generating an evolved bidirectional mapping relationship graph. The evolved bidirectional mapping relationship graph is adaptively optimized using a graph-based evolutionary optimization algorithm, and the optimization stops when the evolutionary trend index meets the preset convergence condition.

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