A data model synchronization method and system based on standardized configuration and intelligent verification

Abstract

The application discloses a data model synchronization method and system based on standardized configuration and intelligent checking, abstract metadata is characterized by using semantic density index and type fingerprint vector, and explicit and implicit business logic constraints are deeply derived, so that a rigorous defense system is constructed before synchronization; a real-time dynamic checking mechanism is introduced, type deviation and logic violation scores can be calculated in time by scanning data flow piece by piece, and the inflow of abnormal data is effectively blocked; adaptive conversion and lossless reconstruction are performed based on risk indexes, heterogeneous type conflicts are solved, and the original information integrity of data is maximally reserved; through terminal state fingerprint comparison and metadata similarity review, a complete closed loop of prevention in advance, blocking in the middle and verification after the fact is formed, the problems of implicit pollution and lagging repair caused by lack of deep logic understanding in traditional technologies are solved, and the reliability, consistency and business availability of cross-source data synchronization are significantly improved.

Description

Technical Field

[0001] This invention relates to the field of data synchronization technology, specifically to a data model synchronization method and system based on standardized configuration and intelligent verification. Background Technology

[0002] In the field of cross-source data model synchronization, existing technologies typically employ fixed mapping rules or simple field type matching mechanisms to achieve data migration and synchronization. Specifically, traditional solutions often rely on manually predefined static mapping tables to directly map source-side fields to target-side fields, and when encountering data type inconsistencies, they employ simple handling methods such as truncation, default value filling, or forced conversion. Furthermore, while some automated synchronization tools can identify basic data type conflicts, they lack the ability to understand the deep business logic, implicit constraints, and dynamic relationships between fields, making it difficult to detect and intercept data anomalies caused by semantic differences or logical contradictions in real time during the synchronization process. This passive mode of "heavy mapping, light validation" makes it highly susceptible to problems such as implicit data pollution, logical breaks, or invalid business rules when facing complex heterogeneous data sources. Moreover, due to the lack of a closed-loop verification mechanism across the entire chain, problems often only become apparent after the data has been written to the target end, resulting in high repair costs and a data trust crisis.

[0003] In existing technologies, due to the lack of intelligent derivation and real-time dynamic verification mechanisms based on standardized metadata features, it is difficult to effectively identify and handle semantic ambiguities, implicit logical conflicts, and type mismatches between fields during data synchronization, resulting in uncontrollable quality of synchronized data and delayed error repair. Summary of the Invention

[0004] This invention aims to provide a data model synchronization method and system based on standardized configuration and intelligent verification, which realizes precise control over the entire process of cross-source data model synchronization. It can identify and block type deviations and logical violations in real time before and during synchronization, ensuring the integrity, consistency and correctness of the target data and business logic.

[0005] To achieve the above objectives, the technical solution adopted by this invention is: a data model synchronization method based on standardized configuration and intelligent verification, comprising:

[0006] Step 1: Standardize and extract the source metadata, and quantify the field attributes into semantic density index and type fingerprint vector to generate a standardized feature map of the source.

[0007] Step 2: Based on the standardized feature map of the source end, dynamically adapt the metadata of the target end, calculate the semantic similarity score between the fields of the source end and the target end, establish the semantic alignment relationship, and generate the intermediate model document;

[0008] Step 3: Based on the intermediate model document, derive explicit and implicit logical constraints, calculate dependency strength coefficients and logical conflict entropy, and form a logical verification rule base.

[0009] Step 4: Based on the aforementioned logical verification rule base and semantic alignment relationship, verify each real-time data stream, calculate the type deviation, correlation breakage index and logical violation score, and dynamically intercept abnormal data;

[0010] Step 5: For the data that passes the verification, perform adaptive conversion and lossless reconstruction based on semantic alignment relationship and type conversion risk index, and calculate reconstruction fidelity;

[0011] Step 6: Perform final state verification on the data written to the target end, compare the similarity between the data fingerprint and the metadata, and complete the difference closed-loop confirmation based on the verification results.

[0012] Preferably, quantifying field attributes into a semantic density index and a type fingerprint vector includes: parsing the source data definition language to extract the original field attributes and mapping them to a standardized metadata template; calculating the semantic density index based on the standardized field attributes; and constructing a type fingerprint vector and calculating its modulus according to the field's data type category, precision level, and null tolerance.

[0013] Preferably, establishing semantic alignment relationships includes: mapping target-side metadata to the same standardized metadata template as the source-side; calculating semantic similarity scores based on field names, type fingerprint vectors, and business contexts; establishing field mapping relationships based on the semantic similarity scores; and calculating type conversion risk indices for aligned field pairs.

[0014] Preferably, the derivation of explicit and implicit logical constraints includes: scanning foreign keys and checking constraints in intermediate model documents to construct a data flow graph; calculating the dependency strength coefficient based on foreign key dependencies; detecting circular dependency paths and calculating logical conflict entropy; and derivation of implicit business logic rules based on historical data samples and calculation of their logical coverage.

[0015] Preferably, the step of verifying each real-time data stream includes: verifying whether the field values ​​conform to the range defined by the type fingerprint vector and calculating the type deviation; using the dependency strength coefficient and the logic verification rule base to verify the association relationship and internal logic consistency between records, and calculating the association break index and logic violation score; summarizing the batch verification results to calculate the shard pass rate and anomaly distribution entropy; and dynamically adjusting the verification strategy based on the shard pass rate and anomaly distribution entropy.

[0016] Preferably, the adaptive conversion and lossless reconstruction includes: reading the type conversion risk index and conversion strategy suggestions for field pairs; selecting a conversion algorithm based on the data distribution characteristics of the source data for high-risk fields, and calculating the reconstruction fidelity; splitting complex structured data according to the data flow diagram, and generating a structure hash value for each segment; identifying synonymous fields and merging redundant information.

[0017] Preferably, the final state verification of the data written to the target end includes: calculating and comparing the data fingerprints of the source and target end tables based on the hash values ​​of key fields; reviewing the abnormal data intercepted in step four and the data that has undergone high-risk transformation in step five; comparing the metadata descriptions of the source and target ends to calculate the metadata similarity; generating a synchronization final state acceptance report, and signing the synchronization completion certificate or entering the difference suspension state according to the report results.

[0018] Preferably, the semantic density index, type fingerprint vector, dependency strength coefficient, logical conflict entropy, and logical coverage are used to support the construction of the logical verification rule base.

[0019] Preferably, the type deviation, correlation fracture index, logical violation score, fragmentation pass rate, and abnormal distribution entropy are used to drive the dynamic interception decision of the real-time data stream.

[0020] On the other hand, this invention proposes a data model synchronization system based on standardized configuration and intelligent verification, comprising:

[0021] The standardized extraction unit is used to standardize the extraction of source metadata and quantify field attributes into semantic density index and type fingerprint vector to generate source standardized feature map;

[0022] The dynamic adaptation unit is used to dynamically adapt the target metadata based on the source-end standardized feature map, calculate the semantic similarity score between the source-end and target-end fields, establish a semantic alignment relationship, and generate an intermediate model document.

[0023] The logic derivation unit is used to derive explicit and implicit logical constraints based on the intermediate model document, calculate the dependency strength coefficient and logical conflict entropy, and form a logic verification rule base.

[0024] The intelligent verification unit is used to verify each real-time data stream based on the logical verification rule base and semantic alignment relationship, calculate the type deviation degree, correlation break index and logical violation score, and dynamically intercept abnormal data.

[0025] The adaptive transformation unit is used to perform adaptive transformation and lossless reconstruction on the data that has passed the verification, based on the semantic alignment relationship and the type conversion risk index, and to calculate the reconstruction fidelity.

[0026] The final state verification unit is used to perform final state verification on the data written to the target end, compare the similarity between the data fingerprint and the metadata, and complete the difference closed-loop confirmation based on the verification results.

[0027] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0028] This invention achieves precise control over the entire process of cross-source data model synchronization by constructing a full-link intelligent management and control process, from metadata standardization and quantification, semantic alignment, and logical constraint derivation to real-time dynamic interception, adaptive lossless reconstruction, and final-state closed-loop verification. Specifically, it utilizes semantic density index and type fingerprint vector to characterize abstract metadata and deeply derive explicit and implicit business logic constraints, thereby building a robust defense system before synchronization. A real-time dynamic verification mechanism is introduced, which can scan the data stream line by line and instantly calculate type deviation and logical violation scores, effectively blocking the inflow of abnormal data. Adaptive transformation and lossless reconstruction are performed based on risk index, resolving heterogeneous type conflicts while preserving the integrity of the original data information to the greatest extent. Through final-state fingerprint comparison and metadata similarity verification, a complete closed loop of prevention, blocking, and verification is formed, solving the problems of implicit pollution and delayed repair caused by a lack of deep logical understanding in traditional technologies, and significantly improving the reliability, consistency, and business availability of cross-source data synchronization. Attached Figure Description

[0029] Figure 1 This is a flowchart of the data model synchronization method based on standardized configuration and intelligent verification according to the present invention;

[0030] Figure 2 This is a block diagram of the data model synchronization system based on standardized configuration and intelligent verification according to the present invention. Detailed Implementation

[0031] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0032] like Figure 1 As shown, this invention proposes a data model synchronization method based on standardized configuration and intelligent verification. By constructing a standardized configuration system and an intelligent verification process, the metadata characteristics of the data model are transformed into calculable quantitative indicators, specifically including:

[0033] Step 1: Standardize and extract source metadata, and quantify field attributes into semantic density indices and type fingerprint vectors to generate a standardized feature map of the source. Specifically, this includes: parsing the source data definition language to extract the original field attributes and mapping them to the standardized metadata template; calculating the semantic density index based on the standardized field attributes; and constructing a type fingerprint vector and calculating its modulus based on the field's data type, precision level, and null tolerance.

[0034] By transforming heterogeneous source metadata into quantifiable semantic density indices and type fingerprint vectors, a digital abstraction and unified representation of the business connotation and type characteristics of fields is achieved. This not only eliminates semantic ambiguity caused by differences in definitions between different data sources, but also accurately characterizes the complexity and constraint strength of fields through modulus calculation, providing a solid, objective, and standardized feature foundation for establishing high-precision semantic alignment relationships and dynamic logic verification in the future.

[0035] Step 2: Based on the standardized feature map of the source end, dynamically adapt the metadata of the target end, calculate the semantic similarity score between the fields of the source end and the target end, establish the semantic alignment relationship, and generate an intermediate model document; specifically, this includes: mapping the metadata of the target end to the same standardized metadata template as the source end; calculating the semantic similarity score based on the field name, type fingerprint vector, and business context; establishing the field mapping relationship based on the semantic similarity score; and calculating the type conversion risk index for the aligned fields.

[0036] By using a unified mapping standard and multi-dimensional semantic similarity calculation, intelligent automatic discovery and accurate matching of field relationships across heterogeneous data sources are achieved, effectively overcoming the lag and subjective bias of traditional manual mapping. At the same time, a type conversion risk index assessment mechanism is introduced to identify and quantify potential data distortion risks in advance, thereby building a high-fidelity, low-risk semantic alignment benchmark when generating intermediate models, laying a reliable basis for subsequent logical verification and lossless reconstruction.

[0037] Step 3: Based on the intermediate model document, derive explicit and implicit logical constraints, calculate dependency strength coefficients and logical conflict entropy, and form a logical verification rule base; specifically, this includes: scanning foreign keys and checking constraints in the intermediate model document, constructing a data flow graph; calculating dependency strength coefficients based on foreign key dependencies; detecting circular dependency paths and calculating logical conflict entropy; and deriving implicit business logic rules based on historical data samples and calculating their logical coverage.

[0038] Specific semantic density index, type fingerprint vector, dependency strength coefficient, logical conflict entropy, and logical coverage are used to support the construction of the logical verification rule base.

[0039] Specifically, type deviation, correlation breakage index, logical violation score, fragmentation pass rate, and anomaly distribution entropy are used to drive dynamic interception decisions for real-time data streams.

[0040] By quantifying dependency strength and logical conflict entropy, static metadata constraints are transformed into a dynamic validation rule base. This not only accurately captures explicit foreign key relationships and implicit business logic, but also effectively identifies and warns of deep logical contradictions such as circular dependencies. Combined with high-coverage rules derived from historical samples, it provides a robust logical defense for real-time data streams, ensuring that type deviations, relationship breaks, and logical violations can be identified and blocked in real time during data synchronization, thus eliminating data pollution caused by logical omissions or conflicts at the source.

[0041] Step 4: Based on the logical verification rule base and semantic alignment relationship, verify each real-time data stream, calculate the type deviation, correlation breakage index and logical violation score, and dynamically intercept abnormal data;

[0042] The process of verifying each real-time data stream includes: verifying whether field values ​​conform to the range defined by the type fingerprint vector and calculating the type deviation; using the dependency strength coefficient and the logic verification rule base to verify the relationship between records and the internal logic consistency, and calculating the association break index and logic violation score; summarizing the batch verification results to calculate the sharding pass rate and anomaly distribution entropy; and dynamically adjusting the verification strategy based on the sharding pass rate and anomaly distribution entropy.

[0043] By performing real-time verification on a line-by-line basis and quantifying multi-dimensional indicators, millisecond-level accurate identification and immediate blocking of data streams with out-of-bounds data types, broken associations, and logical conflicts are achieved, effectively preventing abnormal data from penetrating the target end. At the same time, by dynamically adjusting the verification strategy using shard pass rate and abnormal distribution entropy, not only is the targeting and efficiency of data cleaning greatly improved, but an adaptive defense mechanism is also built to ensure the integrity, consistency, and business continuity of data synchronization in high-concurrency scenarios.

[0044] Step 5: For the data that passes the verification, perform adaptive conversion and lossless reconstruction based on semantic alignment relationship and type conversion risk index, and calculate reconstruction fidelity;

[0045] The adaptive conversion and lossless reconstruction process includes: reading the type conversion risk index and conversion strategy suggestions for field pairs; selecting conversion algorithms based on the distribution characteristics of source data for high-risk fields and calculating the reconstruction fidelity; splitting complex structured data according to the data flow diagram and generating structural hash values ​​for each segment; identifying synonymous fields and merging redundant information.

[0046] By introducing a type conversion risk index and source distribution characteristics, adaptive selection of data conversion strategies and intelligent splitting and reorganization of complex structures are achieved, effectively avoiding data accuracy loss or semantic distortion caused by blind conversion. Combined with reconstruction fidelity assessment and synonymous field merging mechanism, not only is high-fidelity restoration and structural integrity of heterogeneous data ensured during migration, but the quality consistency and business availability of target data are also significantly improved.

[0047] Step Six: Perform final-state verification on the data written to the target end, compare the data fingerprint with the metadata similarity, and complete the difference closure confirmation based on the verification results. This final-state verification includes: calculating and comparing the data fingerprints of the source and target tables based on the hash values ​​of key fields; reviewing the abnormal data intercepted in Step Four and the data that underwent high-risk transformation in Step Five; comparing the metadata descriptions of the source and target ends to calculate metadata similarity; generating a synchronization final-state acceptance report, and signing the synchronization completion certificate or entering the difference suspension state based on the report results.

[0048] By comparing data fingerprints across the entire chain and verifying metadata similarity, a closed-loop quality verification mechanism from source to final state was constructed. This mechanism not only accurately identified and repaired potential data drift and logical deviations, but also enabled retrospective confirmation of anomaly interception and high-risk data conversion. The final acceptance report and difference suspension mechanism provide tamper-proof audit evidence and automated delivery credentials for data synchronization, ensuring the final consistency, traceability, and business credibility of cross-system data flow.

[0049] On the other hand, this invention proposes a data model synchronization system based on standardized configuration and intelligent verification, such as... Figure 2 As shown, it includes:

[0050] The standardized extraction unit is used to standardize the extraction of source metadata and quantify field attributes into semantic density index and type fingerprint vector to generate source standardized feature map;

[0051] The dynamic adaptation unit is used to dynamically adapt the target metadata based on the source-end standardized feature map, calculate the semantic similarity score between the source-end and target-end fields, establish a semantic alignment relationship, and generate an intermediate model document.

[0052] The logic derivation unit is used to derive explicit and implicit logical constraints based on the intermediate model document, calculate the dependency strength coefficient and logical conflict entropy, and form a logic verification rule base.

[0053] The intelligent verification unit is used to verify each real-time data stream based on the logical verification rule base and semantic alignment relationship, calculate the type deviation degree, correlation break index and logical violation score, and dynamically intercept abnormal data.

[0054] The adaptive transformation unit is used to perform adaptive transformation and lossless reconstruction on the data that has passed the verification, based on the semantic alignment relationship and the type conversion risk index, and to calculate the reconstruction fidelity.

[0055] The final state verification unit is used to perform final state verification on the data written to the target end, compare the similarity between the data fingerprint and the metadata, and complete the difference closed-loop confirmation based on the verification results.

[0056] Furthermore, the units in the above system also perform the following other steps to implement the data model synchronization method based on standardized configuration and intelligent verification during execution:

[0057] Step 1: Standardized extraction and feature quantification of source metadata;

[0058] Before any form of data migration, a comprehensive and in-depth analysis of the data model in the source system is performed, transforming unstructured metadata descriptions into a unified standard format. This process is not simply information reading, but a deep deconstruction of Data Definition Language (DDL) statements, aiming to strip away syntactic differences caused by different database dialects and extract pure business semantic features. First, the system traverses all data tables to be synchronized in the source system, scanning their table creation statements and associated definition files row by row, using regular expression matching technology to locate core elements such as field names, data types, length limits, default values, and constraints. For each identified field, its original attributes are mapped to a predefined standardized metadata template. This template mandates that all fields must contain four dimensions of information: a unique identifier, a basic type code, an extended attribute set, and a logical hierarchy marker. During this process, special attention is paid to fields with inconsistent naming conventions across different databases but the same business meaning; for example, user_id, uid, and user ID are uniformly normalized to the standard internal identifier std_user_id to ensure consistency in subsequent processing.

[0059] After extracting the basic information, the next step is feature quantization, which transforms the abstract metadata attributes into concrete numerical vectors for subsequent mathematical operations and logical judgments. For each field's standardized description, its semantic density index is calculated. This index reflects the amount of valid business information contained in the field definition. The calculation formula is as follows:

[0060] ;

[0061] In the formula, represents the number of attributes defined for the field in the standardized template, including specific items such as type, length, and constraints; represents the total character length of the field defined in the original table creation statement; and represents the number of external dictionary tables or enumeration values ​​referenced by the field. The intention behind this formula is that the more concise the field definition and the more reference information it contains, the higher its semantic density, meaning that the business meaning of the field is clearer and its dependencies are stronger. Conversely, a less concise definition may be redundant or vague. This is calculated to obtain... Then, the system will sort all fields in descending order and prioritize processing fields with high semantic density, because they usually carry core business logic and are the focus of subsequent verification.

[0062] After obtaining the semantic density index of each field, it is necessary to further construct the type fingerprint vector of the field. This vector is used to characterize the data feature space of a field. It consists of three components, corresponding to the data type category, precision level, and null tolerance, respectively. Let... The data type category is encoded (e.g., 1 for integers, 2 for floating-point numbers, 3 for strings, etc.), the precision level coefficient is normalized to the 0-1 range based on the field's defined length or decimal places, and the null value tolerance coefficient is defined as the proportion of null values ​​allowed for this field in actual historical data. Then the modulus of the type fingerprint vector is... The calculation formula is:

[0063] ;

[0064] Where and are the weight coefficients of various features, satisfying Furthermore, the weight values ​​are pre-set based on the importance of the business scenario. For example, in financial scenarios, (Precision weight) should be significantly higher than the other two, but in log-type scenarios, The weight for (tolerance for null values) is relatively high. This is achieved through calculation... This can compress complex field attributes into a single scalar value, facilitating quick filtering of fields with abnormal or extreme characteristics. If a certain field's... If the value exceeds the preset threshold, the system will automatically mark the field as a high-risk feature and record its deviation direction to provide a basis for the next step of semantic alignment.

[0065] Finally, based on the above quantification results, a standardized feature map of the source data model is generated. This map uses nodes to represent fields, edges to represent dependencies between fields (such as foreign key associations, cascading updates, etc.), and the node attributes are those calculated earlier. and Equivalent quantification indicators. During the construction of the graph, isolated nodes in the graph require special handling; that is, those nodes that are not associated with other fields and... Fields below the threshold will be marked as potentially redundant by the system and their synchronization eligibility will be temporarily frozen, pending secondary confirmation in subsequent steps.

[0066] Step 2: Dynamic adaptation and semantic alignment of target-side metadata;

[0067] After obtaining the standardized feature map from the source end, the parsing of the target end's data model must be initiated immediately. However, the parsing strategy for the target end cannot simply copy the source end's model; a dynamic adaptation mechanism is required. This is because the target end system may have different storage engines, version differences, or established architectural specifications, and directly applying the source end's standard may lead to compatibility issues. Therefore, the core task of this step is to map the target end's raw metadata to the same standardized framework as the source end, while preserving the target end's unique attributes, forming a dual-view comparison structure. First, the system reads the target end database's structure definition file, extracts its table structure, field definitions, and constraint rules, and fills this information into a standardized metadata template that is completely consistent with the source end. For target end-specific field attributes (such as specific index strategies or partition keys), the system will supplement the records in the template's extended attribute set to ensure that no critical information is lost.

[0068] After completing the basic mapping, the crucial semantic alignment stage begins, which involves finding the optimal matching relationship between source and target fields. Due to differences in naming conventions and data structure designs across different systems, fields with the same name may have different meanings, while fields with different names may represent the same business concept. Therefore, a semantic matching algorithm based on cosine similarity is introduced to calculate the optimal match between source and target fields. With target field semantic similarity score between This score comprehensively considers the edit distance of field names, the angle between type fingerprint vectors, and the relevance of business context. The calculation formula is as follows:

[0069] ;

[0070] In the formula, represents the fingerprint vector of the source field type. fingerprint vector of target field type The cosine similarity between the data types reflects the degree of compatibility between them; it is used for field names. and The Levinstein edit distance between the two fields measures the closeness of their names; is a context association indicator function, which takes a value of 1 if the two fields are on the same business chain or indirectly related through a foreign key, and 0 otherwise; is a weighting coefficient, and The ratio can be adjusted according to the actual business scenario. For example, in systems with strong consistency requirements, the value can be larger.

[0071] Based on the similarity scores, the system constructs a bipartite graph matching matrix, where rows represent source-side fields and columns represent target-side fields. The matrix elements are... Subsequently, the Hungarian algorithm or the maximum weighted matching algorithm is applied to find the globally optimal field pairing scheme in the matrix. This process not only pursues the highest score for a single pair but also emphasizes the stability of the overall matching scheme, avoiding chaotic one-to-many or many-to-one mappings. For field pairs with matching scores exceeding a preset threshold (e.g., 0.85), the system automatically identifies them as aligned fields and records their matching confidence. For field pairs with scores below the threshold but not completely excluded, the system marks them as requiring manual review or potentially ambiguous and generates a detailed difference analysis report. Furthermore, if it is found that a field on the source side cannot find any reasonable matching field on the target side, or if there are a large number of unmatched fields on the target side, the system will trigger a structural missing warning, indicating that the current target side model may not be able to meet the synchronization requirements of the source side data.

[0072] After establishing field pairings, further type compatibility checks are required; this is a crucial step in semantic alignment. Even if two fields are highly similar semantically, direct synchronization can lead to data precision loss or overflow if the underlying data types are incompatible (e.g., the source is a high-precision fixed-point number, while the target is only a single-precision floating-point number). Therefore, for each pair of aligned fields, a type conversion risk index is calculated. This index is based on a comprehensive evaluation of the differences in numerical range, precision loss rate, and bit width between the source and target fields. The calculation formula is as follows:

[0073] ;

[0074] Among them, and , representing the maximum valid values ​​(or precision levels) of the source and target fields respectively, indicating the absolute difference in their numerical representation capabilities; , representing the scaling factor, which takes a high value when the source field needs to be significantly scaled to be stored in the target field, and a low value otherwise; , representing the estimated bit width loss, which is the total bit width of the source field; and , representing the risk weighting coefficient. If... If the safety threshold is exceeded, the system will automatically mark the field as a high-risk conversion and prevent it from entering the synchronization queue directly. Instead, it will enter a special conversion strategy formulation process.

[0075] Finally, based on all the aforementioned analysis results, an intermediate model document adapted to the target side is generated. This document not only includes the field mapping relationship between the source and target sides, but also records in detail the semantic similarity, type conversion risk index, and suggested conversion strategies (such as truncation, rounding, type promotion, etc.) for each pair of fields. The document also includes a list of structural differences, listing fields present in the source side but absent in the target side, and fields present in the target side but absent in the source side, along with the processing priority of these differing fields. This intermediate model document serves as a bridge connecting the source and target sides, eliminating the barriers in their physical structure and semantic expression, and providing a precise reference system for the logical consistency verification in the third step.

[0076] Step 3: Automated derivation of logical constraints and conflict prediction;

[0077] After completing the semantic alignment between the source and target ends and generating the intermediate model document, the automated derivation of logical constraints is immediately initiated. The core objective of this step is to uncover the implicit logical rules within and between data models, and to predict potential conflicts that these rules may cause during synchronization. A data model is not merely a collection of fields, but also a carrier of business rules; fields often have complex logical relationships such as dependencies, mutual exclusions, and cascading updates. Ignoring these logical constraints, even if field types perfectly match, synchronized data may violate business logic, leading to system malfunctions. Therefore, this step requires extracting all explicit and implicit logical constraints from the intermediate model document and transforming them into an executable set of validation rules.

[0078] First, the system performs a deep scan of the table constraints in the intermediate model document, identifying logical rules other than non-null constraints such as primary keys, foreign keys, unique constraints, and check constraints. For each foreign key constraint, the system traces its parent table field, constructing a parent-child dependency chain. Based on this, cascading behavior rules are derived, i.e., how the child table should respond when a record in the parent table is deleted or updated. To quantify the tightness of this dependency, a dependency strength coefficient is defined. The calculation formula is:

[0079] ;

[0080] In the formula, represents the number of child tables that depend on the parent table; represents the total number of times the parent table is referenced (considering many-to-many relationships); represents the complexity score of the relationship between parent and child fields, which depends on the field type matching degree and the directness of the business logic; represents the complexity threshold; and represents the natural constant. This formula shows that when a parent table is referenced by a large number of child tables and the relationship logic is complex, its dependency strength is extremely high, and any modification to the parent table may trigger a chain reaction, requiring close monitoring. This is achieved by calculating the complexity of all foreign key pairs. The system can identify highly sensitive dependency chains, and any logical changes on these chains will be considered high-risk operations.

[0081] Secondly, regarding inter-table constraints, the system uses graph theory to construct a data flow graph, where nodes represent tables and directed edges indicate the direction of data flow (usually determined by foreign keys). Within this graph, the system checks for circular dependencies or logical deadlocks. If a circular dependency is detected, the system further analyzes whether the constraints on the circular path constitute a logical contradiction.

[0082] For example, if a foreign key in table A points to table B, and a foreign key in table B points to table A, and both tables have cascading delete rules set, this will prevent data initialization. To quantify the severity of this logical contradiction, we define logical conflict entropy. The calculation formula is:

[0083] ;

[0084] Where is the number of detected independent circular dependency paths, and is the th... This is an estimated probability of logical conflicts occurring on a given path, derived based on path length, constraint type, and consistent statistics of historical data. A higher value indicates a higher overall probability of logical conflicts within the system. When the threshold is exceeded, the system automatically determines that there is a serious logical defect in the current model configuration, and the synchronization process must be suspended until manual intervention is required for repair.

[0085] In addition to explicit constraints, the system also needs to derive implicit logical rules. This is mainly achieved by analyzing the business semantic relationships between fields.

[0086] For example, if the order amount field exists, the order status field should not be unpaid and the amount should not be negative. These rules are not directly written into the database constraints but are embedded in the business logic. To address this, the system introduces a semantic reasoning engine to generate a set of candidate logical rules based on the field names, types, and context information in the intermediate model document. For each candidate rule, its logical coverage is calculated. This refers to the proportion of valid data records that the rule can cover. The calculation formula is:

[0087] ;

[0088] In the formula, represents the number of historical samples that conform to the rule, represents the total number of samples, and represents the noise tolerance coefficient. If a candidate rule... A value close to 1 indicates that the rule has extremely high universality and should be promoted to a formal validation rule; if A lower value might indicate a misjudgment or an exception, and should be removed. In this way, the system makes the implicit business logic explicit, filling the gaps in database-level constraints.

[0089] After deriving the logical constraints, the system proceeds to the conflict prediction phase. It simulates various operational scenarios during the synchronization process (such as full insertion, incremental update, and partial overwrite), applying the derived logical rules to the simulated data flow to observe whether any violations occur. For each possible conflict scenario, its frequency and severity are recorded, creating a conflict risk heatmap. Each cell in the heatmap represents a conflict type and its probability of occurrence under a specific data distribution. Based on this, the system generates a conflict warning report, clearly indicating which field combinations and operation types are most likely to cause logical contradictions, and providing preventative recommendations, such as preprocessing the source data before synchronization or adding an additional buffer layer at the target end.

[0090] Finally, all the above derivation results are integrated into a logic verification rule base. This rule base contains explicit constraint rules, derived implicit rules, and conflict prediction results, serving as the direct basis for real-time verification in the fourth step. Each rule in the rule base has a unique ID, source description, applicable conditions, and failure threshold, ensuring accurate invocation in subsequent steps.

[0091] Step 4: Intelligent verification and dynamic interception of real-time data streams;

[0092] After completing the standardized extraction of source metadata, dynamic adaptation to the target end, and automated derivation of logical constraints, the system enters the real-time data synchronization execution phase. The core task of this phase is to perform deep validation of the actual flowing data, record by record and field by field. Using the rule base and quantitative indicators generated in the previous steps, decisions are made to grant, admit, or block each record to be synchronized. Unlike static model analysis, this step deals with dynamically changing data flows, requiring extremely high response speed and accuracy in the validation process to ensure that any data that does not conform to preset logic or type specifications cannot flow into the target database. First, the system establishes a data flow pipeline, sending the incremental data or full snapshot generated at the source end, after being sharded, to the validation engine. For each data shard, the system extracts the validation rule set related to the shard's table structure based on the logical validation rule base generated in the third step, and loads the corresponding field mapping relationships and type fingerprint vectors.

[0093] For each record to be synchronized, the system initiates a multi-dimensional feature scan. The first step is a basic type compliance check, which verifies whether the value of each field in the record strictly conforms to the type fingerprint vector determined in the second step. The defined range. For example, if a field is defined as a high-precision fixed-point number (… If the value passed from the source exceeds this precision range, or contains non-numeric characters, it is directly determined as a type mismatch. To quantify the severity of this mismatch, a type deviation is calculated. Its calculation formula is based on the source end value. With the target end's allowed range Relative deviation:

[0094] ;

[0095] In the formula, is the center value of the allowable range at the target end; is a very small positive number to prevent the denominator from being zero. A value greater than 1 indicates that the source value falls completely outside the allowed range of the target value, constituting a serious violation; when... A value between 0 and 1 indicates that the value is within the range but on the edge, potentially triggering a risk of precision loss. If... If the set safety threshold (e.g., 0.8) is exceeded, the system will immediately trigger an anomaly interception, mark the record and isolate it in the error queue, and record the specific deviation value and the cause code for subsequent manual review.

[0096] After passing the basic type check, the logical consistency check begins. This step utilizes the dependency strength coefficient derived in step three. logical conflict entropy As a weight, the relationships between records are verified in real time. For records involving foreign key relationships, the system first queries the target database to see if a corresponding parent record exists. If it does not exist, and no cascading insertion strategy is configured in the rule base, it is determined that the foreign key is missing. To assess the impact of this missing value on overall data integrity, a relationship breakage index is calculated. :

[0097] ;

[0098] Where is the number of parent records successfully matched in the current batch, and is the total number of parent records that the child record should theoretically reference. If A high reading indicates a significant break in key relationships. The system will automatically pause the writing of this batch of data and generate a relationship break warning. In addition, the system will also check the business logic constraints between fields within the record.

[0099] For example, the end time cannot be earlier than the start time, and the amount cannot be negative. These implicit rules have been transformed into executable validation operators in the third step and are now directly applied to the data flow. If a violation of logical constraints is detected, the system calculates a logical violation score. The score is determined by the priority of the violation rule and the semantic density of the violation field. Joint decision:

[0100] ;

[0101] In the formula, is the th The weight of each violation rule is a semantic density index of the violating field. The higher the weight, the more severe the violation, and the more stringent the interception measures the system will take, such as discarding the entire record or marking it as a logical deadlock.

[0102] After completing the above individual checks, the system also needs to perform batch-level statistical checks. For each data shard, the system summarizes the check results of all records and calculates the shard pass rate. and abnormal distribution entropy The slice pass rate reflects the overall quality of the batch of data, and the calculation formula is:

[0103] ;

[0104] Where is the number of records that passed all checks, and is the total number of records in the batch. Anomaly distribution entropy measures the concentration of abnormal records. If anomalies are concentrated in certain specific fields or record types, the entropy value is low; if anomalies are randomly distributed, the entropy value is high. A high entropy value may indicate a systemic failure or network transmission error in the source system. Based on and The system dynamically adjusts the verification strategy for subsequent batches. If If the accuracy falls below the preset minimum standard (e.g., 95%), the system automatically switches to conservative mode, adding extra pre-check steps, or even pausing the synchronization process until manual intervention is required; Maintain a high level and If the speed is low, the system will continue to operate at a normal rate.

[0105] Finally, all verified data is divided into two categories: clean data, which is directly written to the target queue; and anomalous data, which is routed to a dedicated anomaly handling buffer. For anomalous data, the system not only records its original content but also automatically attaches a detailed verification report, including the specific triggering rules, the calculated deviation value, and the associated breakage index. This information forms the basis for root cause analysis and automatic repair in subsequent steps.

[0106] Step 5: Adaptive transformation and lossless reconstruction of heterogeneous data structures;

[0107] After the fourth step of intelligent verification and dynamic interception, the remaining data, although passing basic logical and type checks, still suffers from subtle differences in underlying storage mechanisms, encoding formats, and precision definitions between the source and target ends. Direct writing could lead to data distortion or performance degradation. Therefore, this step aims to perform a deep structural transformation on the verified data, achieving lossless reconstruction from the source physical format to the target physical format. This process is not a simple type conversion, but a refined reshaping based on the type fingerprint vector and conversion risk index generated in the previous steps, ensuring that the data maintains its original business meaning and numerical precision during cross-platform migration.

[0108] First, for each aligned field pair, the system reads the type conversion risk index calculated in the second step. The system provides specific conversion strategy recommendations. For low-risk fields (below a threshold), the system uses standard native type mapping, directly parsing the source binary data and writing it to the corresponding field on the target side. However, for high-risk fields, especially those involving precision loss or bit-width compression, the system activates an adaptive conversion engine. This engine dynamically selects the optimal conversion algorithm based on the actual data distribution characteristics of the source field. For example, when the source is a double-precision floating-point number and the target only supports single precision, the system will not simply truncate the decimal places, but will first calculate the variance of the source data. and mean To determine the dispersion of the data distribution, if the variance is small, it indicates that the data is concentrated, and a rounding strategy can be used; if the variance is large, a segmented quantization strategy should be used, dividing the data into multiple intervals and mapping them to the discrete values ​​available at the target end.

[0109] To quantify the information loss during the conversion process, reconstruction fidelity is introduced. This metric is used to evaluate how closely the transformed data resembles the original data. The calculation formula is as follows:

[0110] ;

[0111] In the formula, represents the original source value, represents the target value after transformation, represents the average value of the source data, and represents the norm order (usually taken as 2 to represent the mean squared error). The closer to 1, the better the reconstruction effect. The system monitors the process in real time. If the fidelity of a certain batch of data is found to be lower than the preset safety line (such as 0.99), the degradation protection mechanism will be automatically triggered, the conversion of the field will be suspended, and an intermediate buffer scheme will be adopted instead. That is, a temporary high-precision field will be created on the target end to temporarily store the data, and the secondary conversion will be performed after subsequent manual confirmation.

[0112] In addition to numerical type conversions, the system also needs to handle complex structural transformations, such as converting nested JSON objects from the source to flattened table structures on the target, or converting array types from the source to relational tables on the target. These transformations rely on the data flow graph and dependency chains constructed in step three. The system traverses the nodes in the data flow graph, identifies complex objects that need to be split, and performs destructuring operations according to predefined splitting rule templates. During destructuring, the system must maintain data integrity constraints to ensure that the split fragments can be re-aggregated using unique association keys. To this end, the system generates a temporary structural hash value for each split data fragment. This hash value contains the original object's ID, split position, and context information. The calculation formula is:

[0113] ;

[0114] Here, is the index position of the data fragment in the original object, and is a summary of the fragment's content. Through comparison... The system can quickly verify the integrity of data after it is written to the target end. If a hash value mismatch is found, the record will be rolled back immediately and an alarm will be triggered.

[0115] After completing type conversions and structural reorganization of all fields, the system performs redundancy elimination. Due to different naming conventions between the source and target ends, there may be synonyms, leading to duplicate information in the data. The system utilizes the semantic density index extracted in the first step. Semantic similarity score in step 2 This identifies and merges duplicate fields. For example, if two fields are found to record a user's mobile phone number and contact number respectively, and both are duplicates... If the data is highly inefficient, the system will archive one field as the primary field and the other as an auxiliary or redundant field to avoid wasting storage space and data redundancy. During this process, the system will record a deduplication log, listing in detail the fields that were merged and the basis for the merge, ensuring the traceability of the operation.

[0116] Finally, the system packages the transformed data into a physical write packet specific to the target end. This packet not only contains the cleaned and transformed data content but also includes complete transformation metadata, including the version of the transformation algorithm used, the fidelity evaluation result, the structure hash value, and anomaly handling records. These packets are sent to the target end's batch write interface, ready to perform the final disk write operation.

[0117] Step 6: Final verification of synchronization results and confirmation of difference closure;

[0118] After completing the adaptive transformation and lossless reconstruction of the data, and writing the processed data to the target end, a comprehensive final-state verification of the synchronization result is necessary. This step is the last line of defense in the entire synchronization process, aiming to confirm that the source data and target data are completely consistent at the logical and physical levels. Any slight difference must be confirmed before synchronization can be declared successful. Unlike the process control in the previous steps, this step focuses on a result-oriented comprehensive comparison. Utilizing the quantitative indicators and rule base accumulated in the previous steps, a deep mirror comparison of the source and target data is performed to ensure zero error in data migration.

[0119] First, the system initiates a full consistency comparison procedure. This procedure compares the data content of the source and target ends table by table and field by field, based on the standardized feature map generated in the first step and the field mapping relationship generated in the second step. To efficiently handle large-scale data, the system employs a strategy combining sampling verification and key fingerprint comparison. For core business tables, the system calculates the data fingerprint for each table. This fingerprint is a unique identifier formed by aggregating the hash values ​​of all key fields in the table. The calculation formula is as follows:

[0120] ;

[0121] In the formula, is the th The hash value of each key field represents the total number of records, indicating an XOR operation. This is achieved by comparing the source and target tables. The system can quickly determine whether the data in the entire table is consistent. If the fingerprints do not match, the system further locates the specific discrepancies and uses the logical verification rule base from the third step to conduct a secondary review of these discrepancies, distinguishing between genuine discrepancies and false positives.

[0122] Secondly, the system performs a special review of the abnormal data and transformation records processed in steps four and five. The system retrieves the abnormal distribution entropy generated in step four. And the reconstruction fidelity calculated in step 5 The system focuses on examining fields that were previously flagged as high-risk or underwent complex transformations. For these fields, the system performs a byte-by-byte comparison to ensure that the transformed data is completely consistent with expectations at the binary level. Simultaneously, the system verifies the dependency strength coefficient derived in step three. To ensure the data is correctly reflected on the target side, check if the foreign key relationships are complete and if the cascading rules are effective. If orphan records or broken links are found on the target side, the system will automatically trigger a difference repair script to attempt to retrieve missing data from the source side or correct the relationships.

[0123] After confirming data content consistency, the system also needs to verify metadata consistency. This involves checking whether the target table structure, indexes, and constraints completely match those of the source. The system then calls the standardized metadata template from the first step again, compares the metadata descriptions of the source and target, and calculates the metadata similarity. :

[0124] ;

[0125] In the formula, represents the number of fields with consistent attributes, represents the total number of attributes, and represents the cosine of the angle between the metadata vectors. If If the data falls below a threshold, it indicates that the target-side structure has drifted. The system needs to generate structural correction suggestions and prompt the administrator to update the target-side model to match the source-side standard. This step ensures that not only the data itself is consistent, but also the containers holding the data remain consistent, thereby maintaining the long-term stability of the system.

[0126] Finally, the system generates a synchronization final acceptance report. This report summarizes all key metrics of the entire synchronization process, including the total amount of data from the source end, the total amount received from the target end, the verification pass rate, conversion fidelity, the number of difference repairs, and the metadata consistency score. The report also lists all differences that were not automatically repaired and their detailed reasons for final manual confirmation by the administrator. If all metrics meet the preset standards (e.g., pass rate 100%, fidelity > 0.999, metadata similarity = 1), and if unresolved differences exist, the system enters a difference suspension state, locking the synchronization task until manual intervention completes the repair and the verification process is rerun.

[0127] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A data model synchronization method based on standardized configuration and intelligent verification, characterized in that, include: Step 1: Standardize and extract the source metadata, and quantify the field attributes into semantic density index and type fingerprint vector to generate a standardized feature map of the source. Step 2: Based on the standardized feature map of the source end, dynamically adapt the metadata of the target end, calculate the semantic similarity score between the fields of the source end and the target end, establish the semantic alignment relationship, and generate the intermediate model document; Step 3: Based on the intermediate model document, derive explicit and implicit logical constraints, calculate dependency strength coefficients and logical conflict entropy, and form a logical verification rule base. Step 4: Based on the aforementioned logical verification rule base and semantic alignment relationship, verify each real-time data stream, calculate the type deviation, correlation breakage index and logical violation score, and dynamically intercept abnormal data; Step 5: For the data that passes the verification, perform adaptive conversion and lossless reconstruction based on semantic alignment relationship and type conversion risk index, and calculate reconstruction fidelity; Step 6: Perform final state verification on the data written to the target end, compare the similarity between the data fingerprint and the metadata, and complete the difference closed-loop confirmation based on the verification results.

2. The data model synchronization method based on standardized configuration and intelligent verification according to claim 1, characterized in that, The process of quantifying field attributes into a semantic density index and a type fingerprint vector includes: parsing the source data definition language to extract the original field attributes and mapping them to a standardized metadata template; calculating the semantic density index based on the standardized field attributes; and constructing a type fingerprint vector and calculating its modulus according to the field's data type category, precision level, and null tolerance.

3. The data model synchronization method based on standardized configuration and intelligent verification according to claim 1, characterized in that, The establishment of semantic alignment relationships includes: mapping the target-side metadata to the same standardized metadata template as the source-side; calculating semantic similarity scores based on field names, type fingerprint vectors, and business contexts; establishing field mapping relationships based on the semantic similarity scores; and calculating type conversion risk indices for aligned field pairs.

4. The data model synchronization method based on standardized configuration and intelligent verification according to claim 1, characterized in that, The derivation of explicit and implicit logical constraints includes: scanning foreign keys and checking constraints in intermediate model documents to construct a data flow graph; calculating dependency strength coefficients based on foreign key dependencies; detecting circular dependency paths and calculating logical conflict entropy; and derivation of implicit business logic rules based on historical data samples and calculation of their logical coverage.

5. The data model synchronization method based on standardized configuration and intelligent verification according to claim 1, characterized in that, The step-by-step verification of real-time data streams includes: verifying whether field values ​​conform to the range defined by the type fingerprint vector and calculating the type deviation; using the dependency strength coefficient and the logic verification rule base to verify the association relationship and internal logic consistency between records, and calculating the association break index and logic violation score; summarizing the batch verification results to calculate the shard pass rate and anomaly distribution entropy; and dynamically adjusting the verification strategy based on the shard pass rate and anomaly distribution entropy.

6. The data model synchronization method based on standardized configuration and intelligent verification according to claim 1, characterized in that, The adaptive conversion and lossless reconstruction process includes: reading the type conversion risk index and conversion strategy suggestions for field pairs; selecting a conversion algorithm based on the distribution characteristics of the source data for high-risk fields and calculating the reconstruction fidelity; splitting complex structured data according to the data flow diagram and generating a structure hash value for each segment; identifying synonymous fields and merging redundant information.

7. The data model synchronization method based on standardized configuration and intelligent verification according to claim 1, characterized in that, The final verification of the data written to the target end includes: calculating and comparing the data fingerprints of the source and target tables based on the hash values ​​of key fields; reviewing the abnormal data intercepted in step four and the data that has undergone high-risk transformation in step five; comparing the metadata descriptions of the source and target ends to calculate the metadata similarity; generating a synchronization final state acceptance report, and signing the synchronization completion certificate or entering the difference suspension state based on the report results.

8. The data model synchronization method based on standardized configuration and intelligent verification according to claim 4, characterized in that, The semantic density index, type fingerprint vector, dependency strength coefficient, logical conflict entropy, and logical coverage are used to support the construction of the logical verification rule base.

9. A data model synchronization method based on standardized configuration and intelligent verification according to claim 5, characterized in that, The type deviation, correlation fracture index, logical violation score, fragment pass rate, and abnormal distribution entropy are used to drive the dynamic interception decision of real-time data streams.

10. A data model synchronization system based on standardized configuration and intelligent verification for implementing the method as described in any one of claims 1-9, characterized in that, include: The standardized extraction unit is used to standardize the extraction of source metadata and quantify field attributes into semantic density index and type fingerprint vector to generate source standardized feature map; The dynamic adaptation unit is used to dynamically adapt the target metadata based on the source-end standardized feature map, calculate the semantic similarity score between the source-end and target-end fields, establish a semantic alignment relationship, and generate an intermediate model document. The logic derivation unit is used to derive explicit and implicit logical constraints based on the intermediate model document, calculate the dependency strength coefficient and logical conflict entropy, and form a logic verification rule base. The intelligent verification unit is used to verify each real-time data stream based on the logical verification rule base and semantic alignment relationship, calculate the type deviation degree, correlation break index and logical violation score, and dynamically intercept abnormal data. The adaptive transformation unit is used to perform adaptive transformation and lossless reconstruction on the data that has passed the verification, based on the semantic alignment relationship and the type conversion risk index, and to calculate the reconstruction fidelity. The final state verification unit is used to perform final state verification on the data written to the target end, compare the similarity between the data fingerprint and the metadata, and complete the difference closed-loop confirmation based on the verification results.

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