Method and system for implementing cross-database rest interface based on tag dynamic configuration

Abstract

The application discloses a cross-database Rest interface implementation method and system based on tagging dynamic configuration, relates to the technical field of interface dynamic deployment and structure configuration management, and is used for solving the problems of structure change adaptation difficulty, data source dynamic binding difficulty and multi-source query fusion complexity; by defining interface tags, structure snapshots and field triple mapping relations, identifying unstable structure fields and constructing a structure drift coupling graph, structure dynamic adaptation is realized. The system supports setting cache and paging according to interface granularity, supports structure snapshot regular collection and version management mechanism, and guarantees interface availability and consistency in the structure change process. The method has deployment automation, structure fault tolerance and execution dynamic scheduling capability, and is suitable for REST interface management systems in a multi-source heterogeneous database fusion scene.

Description

Technical Field

[0001] This invention relates to the field of interface dynamic deployment and structure configuration management technology, and more specifically, to a method and system for implementing cross-database REST interfaces based on tag-based dynamic configuration. Background Technology

[0002] In existing multi-source data management systems, REST interfaces, as the primary means of data interaction, typically rely on static structure definitions and manual deployment, making it difficult to meet the actual needs of frequent structural changes and dynamic binding across multiple databases. Especially in large platforms, different data sources differ significantly in field definitions, path attribution, naming conventions, and data types. Traditional static configuration mechanisms cannot effectively respond to structural shifts. If the fields bound to the interface are not adapted in time after structural changes, problems such as empty field values, abnormal response times, and type mismatches can easily occur, potentially leading to interface failure or data misrepresentation in severe cases.

[0003] Furthermore, most existing interface construction mechanisms lack automation capabilities, requiring developers to manually write controller logic, bind routing paths, and specify SQL statements, which cannot support scenarios with high-frequency changes and dynamic deployments. For multi-source scenarios that require extracting data from multiple databases, existing solutions generally lack a unified data fusion mechanism, leading to frequent issues such as duplicate queries, structural conflicts, and inconsistent responses. Structural drift detection mechanisms mostly rely on low-frequency version difference comparisons and do not introduce real-time field behavior monitoring and response anomaly analysis, making it difficult to capture runtime field semantic shifts.

[0004] To address the above problems, this invention proposes a solution. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for implementing a cross-database REST interface based on tag-based dynamic configuration, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a preferred embodiment, it includes:

[0008] Build a data source tag registration mechanism and load a structured configuration file, and perform multi-tag connection pool initialization and mapping registration;

[0009] It provides an interface definition configuration channel, supports multi-tag operation structure parsing and field structure consistency verification, and performs trial run verification;

[0010] Based on structural drift features and response behavior sequences, a response heterogeneous coupling window is constructed to identify structurally unstable fields and establish a drift index mapping and partition classification structure.

[0011] It automatically generates interface controller logic and registers it to the routing mapping table, executes dynamic SQL, multi-source concatenation, cache control and interface cleanup processes, and supports hot updates of structure snapshots and interface redistribution.

[0012] In a preferred embodiment, multiple data source tag items defined in the configuration file are loaded, a mapping relationship between tags and connection pools is established, an interface definition configuration channel is provided, and interface tags, paths, methods, parameters, return fields and operation content are filled in. After submission, structure verification and parameter verification are performed, and trial run verification and structure result comparison are performed.

[0013] In a preferred embodiment, structural attribution modeling and dynamic feature extraction are performed on the fields bound in the interface, and the data table level depth, field update time fluctuation range, field hit rate variability and field response stability of each field are statistically analyzed.

[0014] Based on the hit rate variability and response stability, a field call cycle response spectrum is constructed to identify frequency jump cycle segments, and the response drift error frequency is calculated by combining the structure update timestamp.

[0015] In a preferred embodiment, the mean of the path overlap and flattening depth distribution of the calculated field in the multi-label template is combined with the data table level depth to generate the structural penetration coefficient, and the fields that meet the structural penetration threshold and response drift error frequency criterion are selected to form a set of structural frequency dominant fields.

[0016] Least common period extraction and wave energy spectrum analysis are performed on the set of dominant structural frequency fields to extract concentrated wave energy segments and determine the duration of the response frequency coupling window accordingly.

[0017] In a preferred embodiment, the duration of the response frequency coupling window is used as a fixed sliding analysis period to construct a behavioral regression and frequency analysis window. Multi-window linear fitting is performed on the field hit rate and response duration sequence, and fields with structural dynamic drift tendency are identified by combining the fitting residuals and fluctuation characteristics and included in the structurally unstable field set.

[0018] In a preferred embodiment, a triplet mapping relationship is constructed between field binding path, structure snapshot version and interface template position to form field structure evolution trajectory and horizontal comparison view, extract features such as field response time lag, value vacancy rate and type matching error, and identify drift gap areas;

[0019] We construct a structural drift coupling graph with field triples as the core, depict the path change and type migration process of fields between structural snapshot versions, and classify structural drift patterns into three types: field type replacement drift, field name replacement drift, and field path sinking drift.

[0020] In a preferred embodiment, structurally unstable fields are mapped to the interface configuration context, three typical configuration error scenarios—field type drift, naming drift, and path migration—are identified, a two-dimensional structural drift index matrix is ​​established, and structural drift coupling partitions are divided.

[0021] Extract the field structure change path and determine the corresponding drift mode. Construct a field semantic misalignment probability model within the response frequency coupling window, generate a typical configuration error scenario matching rule set and load it into the pre-verification channel. Perform path, type and naming consistency verification on structure-sensitive fields, and construct three types of structure adaptation rules to achieve configuration fault tolerance adaptation.

[0022] In a preferred embodiment, after completing the interface configuration parameter verification and structure pre-verification, the interface automated generation process is executed to generate controller source code based on interface tags, parameter structure, data source tags and SQL operation definitions and register it to the routing mapping table.

[0023] During the interface execution process, the system performs parameter extraction and validation, data source binding and dynamic routing, SQL construction and execution, result extraction and field mapping, multi-source concatenation and field structure validation, and reclaims context resources after the request ends. The scheduler periodically collects snapshots of the data source structure, and the interface redeployer supports uninterrupted redeployment and version management after changes to interface definition items.

[0024] In a preferred embodiment, it includes: a multi-label data source construction module, an interface definition and trial operation verification module, a structure drift identification and mapping partition module, a controller generation and dynamic execution management module, and signal connections between the modules;

[0025] The multi-tag data source building module is mainly used to build a data source tag registration mechanism and load a structured configuration file, and complete the initialization and mapping registration of the multi-tag connection pool;

[0026] The interface definition and trial operation verification module is mainly used to provide an interface definition configuration channel, support multi-tag operation structure parsing and field structure consistency verification, and perform trial operation verification.

[0027] The structural drift identification and mapping partitioning module is mainly used to construct a response heterogeneous coupling window based on structural drift features and response behavior sequences, identify structurally unstable fields, and establish a drift index mapping and partitioning classification structure.

[0028] The controller generation and dynamic execution management module is mainly used to automatically generate interface controller logic and register it to the routing mapping table, execute dynamic SQL, multi-source concatenation, cache control and interface cleanup processes, and support hot updates of structure snapshots and interface redistribution.

[0029] The technical effects and advantages of this invention, which is based on a tag-based dynamic configuration method and system for cross-database REST interfaces, are as follows:

[0030] This invention effectively identifies structurally unstable fields and their drift gaps by defining structural snapshots, field triple mappings, and structural drift coupling maps, achieving dynamic structural adaptation. It employs tag-based configuration and template parsing to automatically map field behaviors in interface definitions, significantly improving adaptability under structural changes. Through an interface code generator and dynamic route registration mechanism, it supports automatic generation and hot deployment of interface logic, reducing development workload and supporting uninterrupted interface updates. It introduces runtime data source context binding and dynamic routing mechanisms to ensure that interfaces can dynamically switch target database connections based on tags, adapting to multi-source heterogeneous scenarios. Through a unified SQL executor and multi-source data fusion logic, it achieves structural consistency and semantic uniformity of results across data sources. Interface responses support parameter validation, pagination, and caching strategies, further improving interface execution efficiency and stability. A structure collector and interface republisher construct a hot update and version management mechanism for interfaces, ensuring stable system operation under structural changes. Attached Figure Description

[0031] Figure 1 This is a timing diagram of the implementation method and system of the cross-database REST interface based on tag-based dynamic configuration of the present invention.

[0032] Figure 2 This is a schematic diagram of the implementation method and system modules of the cross-database REST interface based on tag-based dynamic configuration of the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example: This invention discloses a method for implementing a cross-database REST interface based on tag-based dynamic configuration, such as... Figure 1 As shown, it includes:

[0035] Build a data source tag registration mechanism and load a structured configuration file to complete the initialization and mapping registration of the multi-tag connection pool.

[0036] It provides an interface definition configuration channel, supports multi-tag operation structure parsing and field structure consistency verification, and performs trial operation verification.

[0037] Based on structural drift characteristics and response behavior sequences, a response heterogeneous coupling window is constructed to identify structurally unstable fields and establish a drift index mapping and partition classification structure.

[0038] It automatically generates interface controller logic and registers it to the routing mapping table, executes dynamic SQL, multi-source concatenation, cache control and interface cleanup processes, and supports hot updates of structure snapshots and interface redistribution.

[0039] During the initialization and interface configuration phase, in order to achieve dynamic binding of tags across databases, the configuration file that supports multiple configuration formats is first loaded during the deployment phase, including local YAML files, JSON files, or remote configuration center service nodes.

[0040] The configuration file defines all available data source tags item by item. Each item explicitly includes the data source tag name, the corresponding database type, the fully qualified name of the JDBC driver class, the database connection URL, the authentication username and password, as well as core parameters related to the connection pool such as the minimum number of idle connections, the maximum number of active connections, and the connection timeout.

[0041] The source of the above configuration items depends on the agreed format of the deployment file, ensuring that the data structure is checked for field integrity and format validity by the configuration validator before loading.

[0042] During the application startup phase, according to the contents of the aforementioned configuration file, the configuration resolver, which is an extension of the Spring Boot configuration binding mechanism, is called item by item to map each data source tag to a structured configuration entity object. Subsequently, the factory method of the connection pool builder HikariCP is called to generate a corresponding physical connection pool object for each structured configuration entity, and the connection pool initialization logic is executed, including driver loading, connection warm-up, and connection pool parameter injection.

[0043] After construction, a binding mapping relationship is established between each data source tag and its corresponding connection pool object, and registered to the runtime data source mapping dictionary;

[0044] This mapping dictionary uses a thread-safe ConcurrentHashMap structure, with the tag name as the key and the connection pool object as the value, to ensure access consistency in a multi-threaded environment.

[0045] After completing the data source tag registration, a REST interface definition configuration channel is provided as the entry point for developer interface registration. Users fill out the interface definition form through the front-end web configurator. This form requires specifying a unique REST interface tag, interface request path, HTTP request method, parameter name and type definition, return field format description, whether to enable pagination mechanism, whether to enable response caching, and the data source tag and specific operation definition bound to the interface.

[0046] The operation definition supports three types of operation modes:

[0047] One is the native SQL statement, written directly as a complete text file;

[0048] Secondly, there are dynamic SQL statements with placeholders, which support subsequent parameter injection;

[0049] Thirdly, it features multi-data source combination operation configuration, supporting subqueries or JOIN operations under multiple tags, with each paragraph bound to different tags.

[0050] Next, after the user submits the interface configuration items, the structure validator first performs structural consistency verification on the interface configuration content. The structure validator parses the SQL statement or operation definition, extracts all named parameter placeholders, and matches them one by one with the parameter set declared in the parameter definition. If there are problems such as missing, redundant, or inconsistent types, a structure verification error message is immediately returned.

[0051] After the structure validation is complete, the data source router locates the corresponding connection pool object based on the data source tag bound in the interface configuration and constructs a trial SQL statement with test values. Subsequently, the unified SQL executor calls the connection pool to execute the test query, and the returned result structure is validated for field structure and type alignment by the return structure validator. If the trial result fails to meet the return structure defined in the interface configuration, the system sends this error back to the development team as a configuration phase blocking message, prompting the developer to modify the configuration until the structure definition matches the execution result.

[0052] It should be noted that during the REST interface configuration phase, when binding multiple tags and executing field template parsing, if the database corresponding to a tag undergoes non-destructive structural changes at the structural layer, such as field type modification, field name renaming, or field anonymization strategy adjustment (e.g., replacing integer fields with encrypted strings or plaintext fields with masked fields), the original field-parameter mapping template in the interface registry will deviate from the actual structure of the current field. Furthermore, because the field structure snapshot in the initial interface configuration cache has not established a binding mapping relationship with the historical structure version of the tag fields, the configurator will use the cached field template for parameter generation and interface registration during the runtime phase, thus causing field mapping drift.

[0053] This can lead to parameter type conflicts, incompatible field value formats, or mismatched query structures. At best, it can cause the API execution to be interrupted and throw a syntax exception; at worst, it can return a misstructured dataset under the illusion of successful API execution, resulting in logical errors in the query results that are difficult to detect. This is especially true in API configurations involving multiple tags, nested subqueries, or merged queries, where the field structures originally bound to each tag may reside in different databases, and these fields may change asynchronously due to version updates.

[0054] In the current solution, the field parameter template only maintains a field snapshot once. During the merging phase, the field parameter parser flattens the field definitions of multiple tags into a unified template, ignoring the structural differences of each tag's field. This results in structural mixing, field name conflicts, and type normalization errors in the final generated field binding template. At runtime, it cannot correctly distinguish field semantics, ultimately leading to misparsing of field values, misbinding of cross-tag field values, and misaligned query logic. Therefore, in this embodiment, before performing dynamic tag configuration, to identify potential stability issues in field parameter binding under structural evolution, the tag call history and field response time sequence recorded in the daily operation log are first used as the original sample set to perform structural attribution modeling and dynamic feature extraction on all fields bound to tags in the current interface.

[0055] Specifically, for each field called in the interface, its field binding path in the various tag configuration templates is parsed, and a structural hierarchy sequence of the field is constructed based on the data table hierarchy pointed to by the path. This structural hierarchy sequence is represented by the path stack of the field in the database logical structure, and the data table hierarchy depth is extracted by the structure snapshot collector through the system tables.

[0056] After the structure affiliation is determined, for each field path, the following four types of dynamic indicators are extracted:

[0057] Table hierarchy depth: Calculated by the number of table hierarchy nodes appearing in the field path, directly mapping the physical structure nesting depth of the fields;

[0058] Field update time fluctuation range: Read the data table structure update timestamp sequence corresponding to the field from the structure change log, and calculate the maximum time interval change and the standard deviation of the update frequency to reflect the instability of the structure evolution;

[0059] Field hit rate variability: In the daily API call history, the number of times a field is called is counted using a sliding window method, and the standard deviation within the period is calculated to identify the volatility of its usage frequency;

[0060] Field response stability: Based on the field response time series, local variance calculation is performed within a fixed window, and the existence of high-frequency mutation points is identified to determine the degree of fluctuation in field response behavior.

[0061] Next, using field hit rate variability and field response stability as the two core variables, a field call cycle response spectrum is constructed. This spectrum plots time on the horizontal axis and hit rate and response duration on the vertical axis, showing the behavior of each field across multiple consecutive call cycles. A periodic sliding difference algorithm is used to identify time windows in the spectrum where the hit rate continuously decreases and the response duration exhibits periodic sawtooth oscillations, marking these as frequency jump period segments.

[0062] Subsequently, combining the structure update timestamps recorded in the aforementioned field update time fluctuation range, the structure update time points are aligned to the frequency jump period segments, and the time offset ratio between the structure update time point and the field response trough is calculated. This time offset ratio is defined as the response drift error frequency, used to measure the temporal coupling strength between field structure evolution behavior and runtime response instability. The statistics for this time offset ratio are based on the timestamp alignment mechanism between the structure update log and the response behavior log, ensuring that the error frequency determination has a clear data source.

[0063] The response drift error frequency is defined as:

[0064] η(t) = Δt_drift / T_window;

[0065] Where Δt_drift is the minimum time difference between the structure update time and the response trough, and T_window is the total duration of the sliding time window N1. η(t) reflects whether the structural change causes periodic disturbances to the field response behavior.

[0066] Next, the path overlap for each field across multiple tag configurations is calculated. Specifically, based on the end paragraph of the path bound to the field in each template, the proportion of path consistency is calculated, and a binding overlap index is generated. Simultaneously, combining the normalized depth value of the field in the flattened template (i.e., the flattened index position of the field in the unified template), the average flattening depth distribution of the field across all tag templates is calculated, and combined with the original data table hierarchy depth, a field structure penetration coefficient is generated.

[0067] The field structure penetration coefficient is defined as follows:

[0068] Λ(t,s)=(D_flat(t,s) / D_raw(t,s))×log(1+L_path(t,s));

[0069] Where D_flat(t,s) is the average normalized depth of the field in the flattening template, D_raw(t,s) is the hierarchical depth of the field in the original data table, and L_path(t,s) is the length of the field path in the structure snapshot. This formula reflects the degree of penetration of the field structure path after flattening and is used to measure the complexity of field parsing.

[0070] When the structural penetration coefficient of a field exceeds the set structural penetration threshold Λ0, and its response drift error frequency exceeds the structural update interference threshold λ0 in two or more frequency jump cycles, then the field is included in the set of dominant structural frequency fields. This set of dominant structural frequency fields represents a subset of fields with deep structural path penetration levels and significant frequency errors in response behavior, and is a key object affecting response stability due to structural evolution.

[0071] The recommended value for the structural penetration threshold Λ0 is 2.5–3.5, with the specific value determined by the mean plus two standard deviations of the historical sample distribution of the structural penetration coefficient. The response drift error frequency λ0 is set to 0.25, meaning that the time offset between the structural update time and the response anomaly must account for more than 25% of the cycle length to be considered significantly correlated with the error frequency. These thresholds are derived from statistical analysis of the operational logs of 100 typical multi-tag interfaces over three months.

[0072] Within the dominant structural frequency field set, least common period extraction is performed on the frequency jump period sequence of each field. This process uses the intersection of periodic integer sequences to obtain the synchronization rhythm of the behavioral responses of multiple fields, extracting the minimum common period value as a candidate analysis window period. Subsequently, using the fluctuation bandwidth of the response values ​​of each field within this candidate period segment, i.e., the difference between the maximum and minimum response values, as input, fluctuation energy spectrum analysis is performed to calculate the degree of fluctuation energy concentration. Based on the set high energy density interval threshold, periodic segments with high energy concentration are selected and defined as fluctuation energy concentration segments.

[0073] Then, the minimum continuous time window length is extracted from the concentrated wave energy segment, and this length is defined as the duration of the response heterogeneous coupling window.

[0074] It should be noted that the window timescale of this final sliding analysis has the characteristics of behavioral frequency error driving under the background of structural penetration, which can accurately capture the field response instability process triggered by structural evolution, and provide time domain support for the dynamic identification of structurally unstable field sets.

[0075] Subsequently, the duration of the response frequency coupling window is defined as N1. A sliding analysis window is constructed with a fixed time interval N1. Within each window period, multi-window linear regression fitting is performed on the hit rate sequence and response duration sequence of the field, and fluctuation frequency analysis is performed based on the fitting residuals and local fluctuation variance.

[0076] In this analysis process, the field response sequence fitting module is first invoked. The behavior sequence within the sliding window is used as input to construct a linear fitting curve for the field in the time-response space. Then, the fitting correlation coefficient and the rate of change of the fitting slope are calculated to construct the correlation determination function.

[0077] Meanwhile, using an improved periodic fluctuation analysis algorithm, the period of change of the first derivative of the response value and the peak-trough density within each sliding window are extracted to generate a fluctuation threshold function to determine whether the response behavior exhibits high-frequency disturbances dominated by the structure.

[0078] When a field's correlation coefficient continuously decreases, its fluctuation amplitude and frequency continuously increase over multiple sliding window periods, and the fitted curve exhibits inflection point accumulation or slope oscillation characteristics, the field is labeled as having a significant tendency for structural dynamic drift and is included in the set of structurally unstable fields.

[0079] This process forms a dynamic structural stability identification mechanism supported by behavioral fitting trends and defined by a time window. The size of the sliding window used to perform the above analysis is defined as the label drift decision window.

[0080] Furthermore, after identifying the unstable field set, a mapping relationship of tag field, structure snapshot version, and interface template position is first constructed based on the field binding path and its binding position in the interface template. Then, by analyzing the differences in the relationship between the physical path of the field in the structure snapshot and its template mapping position, the evolution trajectory of the field under different structure versions is established, and a horizontal comparison view of the field structure parsing behavior is formed.

[0081] Based on this, three types of abnormal features related to field semantic offsets are further extracted:

[0082] Field response time lag: This refers to the delay segment where the response time increases significantly after a field structure update occurs;

[0083] Field value vacancy rate: The percentage of empty or missing values ​​in the returned results for a field;

[0084] Type matching error: The difference between the type of the field return value and the type expected by the interface template, including format incompatibility and type inference failure.

[0085] By cross-analyzing the above three types of indicators, we can identify the mapping gaps between the field structure and the template parsing, which are called drift gaps. These drift gaps are high-risk areas where structural changes are not synchronously recognized by the template, resulting in the field semantics shifting during runtime.

[0086] Subsequently, using the aforementioned field triples as the core of the analysis, a structural drift coupling graph is constructed. This graph uses fields as nodes, structural snapshot versions as state edges, and interface template behaviors as attached labels, characterizing the path changes, type migrations, and naming transformations of fields during structural evolution. Furthermore, based on the type of field change events and the scope of their semantic impact, the structural drift patterns are categorized into three main types within this graph:

[0087] Field type replacement drift: The field type marked in the structure snapshot is replaced from numeric to masked character or encrypted type, which causes the original defined field in the interface template to be inconsistent with the actual field type. The type inference failure causes the field to be automatically set to empty or inserted with a default value during the concatenation process, thereby disrupting the field concatenation order.

[0088] Field name replacement drift: The structure log records that the field name or alias has changed, but the interface template cache has not been refreshed synchronously. When constructing the query, the old field name is still used, which causes the field to fail to hit the structure. The returned result shows missing fields or field mismatch, semantic offset without reporting an error.

[0089] Field path sinking and drifting: After the physical structure of a field is migrated to a sub-table, split table, or related table, the interface template is still bound to the old path field. During parsing, the target field cannot be hit, so it is mistakenly bound to a field with a similar structure or returned as empty, ultimately causing the field value context misalignment or semantic confusion.

[0090] Based on the above structural drift map construction, the identified structurally unstable fields are further mapped to specific interface configuration contexts. According to the correspondence between field attribution path, field change type and interface splicing behavior, the following three typical configuration error scenarios are constructed:

[0091] Scenario 1: Field type drift causes concatenation errors: In the configuration of multi-tag merging, a field was originally an integer, but was later updated to a mask string. The interface merging template still concatenates according to the original type. The type inference fails, causing the concatenation length to overflow or be truncated, resulting in data structure expansion or field loss.

[0092] Scenario 2: Field naming drift leads to implicit field missing: Field aliases are updated but the template cache is not refreshed, the interface still calls the old field names, the query returns empty values ​​but no error is reported, the result structure is complete but the field meanings are wrong, which is difficult to detect;

[0093] Scenario 3: Field path migration causes value binding offset: When a field is migrated from the main table to the child table, the interface template does not complete the path redirection. After the binding fails, it defaults to matching fields with similar structures, resulting in misreference of field values ​​or misalignment of context semantics.

[0094] Furthermore, the identified set of structurally unstable fields is mapped to a two-dimensional structural drift mapping region. This mapping region uses field attribution path information and response behavior sequence features as a joint index dimension to ensure simultaneous modeling of structure and behavior, as detailed below:

[0095] Field-attributed path dimension: Read the binding path of the field in the tag template structure, parse the path stack to which the field belongs into a structural hierarchy depth sequence through the path parser, and partition it according to the path prefix to form a path partition index space;

[0096] Response behavior dimension: Within the N1 time window, combining the two feature dimensions of response drift error frequency and fluctuation energy concentration, a perturbation map builder is invoked to generate a field response error frequency perturbation map. This map, with time as the horizontal axis and energy concentration distribution as the vertical axis, maps the correlation between the field's response stability and structural evolution.

[0097] By jointly indexing the two dimensions mentioned above, a two-dimensional structure drift index matrix is ​​constructed. In the structure drift index matrix, the minimum structural penetration depth of the field in the path partition is used as the primary index dimension, representing the deepest level at which the field is referenced during structural flattening; the periodic perturbation feature frequency extracted from the response perturbation map is used as the secondary index dimension, marking the intensity and frequency of periodic misalignment of the field at the behavioral level.

[0098] Subsequently, multidimensional sparse clustering analysis was performed on the structure drift index matrix, using a sparse K-means-S variant algorithm to adaptively cluster the feature points of the fields in the matrix. The objective function of clustering combines structural depth compactness and response perturbation homogeneity, ultimately dividing the matrix into several structure-response coupling clusters. Fields within each coupling cluster share commonalities such as similar nested structural paths and convergent response error frequency characteristics. Next, the coupling clusters are defined as structure drift coupling partitions, serving as the smallest unit for subsequent classification and scenario matching.

[0099] The number of clusters k is dynamically determined using silhouette coefficient analysis. The average silhouette coefficient corresponding to all possible k values ​​is calculated, and the optimal value is selected. The calculation of the silhouette coefficient S(i) follows the following rules:

[0100] S(i)=(b(i)-a(i)) / max{a(i),b(i)};

[0101] Where a(i) is the average distance from sample i to other samples in the same cluster, and b(i) is the average distance from sample i to the nearest other cluster. If the maximum average silhouette coefficient is less than 0.5, the current data is considered unsuitable for clustering. The vectors of field feature points are Z-score standardized before clustering to ensure dimensionality balance.

[0102] Furthermore, within each structural drift coupling partition, the historical structural version trajectory of the field is extracted. This trajectory comes from the structural change records of the field across versions in the structural snapshot collector. Combined with the field registration snapshot in the interface template cache, the actual binding records of the field on the structural evolution path are constructed, thereby generating a field structural change path sequence. Then, pattern recognition is performed on the structural variation behaviors in the path sequence. Based on whether the change path involves field type replacement, field name renaming, or field path migration chain breakage, the following three types of structural drift patterns are determined:

[0103] TypeReplace mode: When the field type changes from numeric to masked character or encrypted type in the structure snapshot, the binding template is not updated synchronously, and the type incompatibility causes the splicing failure or the field to be empty.

[0104] NameReplace mode: When the field name or alias changes at the database structure level, the template is not updated synchronously, and the field name cannot be matched with the template, resulting in missing or misaligned fields.

[0105] PathSink mode: When a field is migrated to a sub-table, split into multiple tables, or its path is changed, the interface template is still bound to the old path. When a field fails to match, it falls back to binding a similar field, resulting in semantic mismatch.

[0106] Furthermore, to ensure that the pattern determination has time boundary constraints and behavior-driven characteristics, the structural evolution behavior is sampled and judged only within the response frequency coupling window defined by N1, ensuring that all classification logic is only effective when response anomalies occur, thus avoiding misjudgment of structural drift.

[0107] After determining the structural drift pattern, the structural risk modeler is invoked, using structural drift coupling partitions as units, to construct a field structural semantic misalignment probability model. This model uses structural change path type, response volatility, field vacancy rate, and historical binding mismatch frequency as input variables to calculate the probability of a field undergoing semantic shift in the current template.

[0108] Based on this, a set of matching rules for typical configuration error scenarios is further defined. Each rule consists of the following three-layer structure:

[0109] Time-triggered condition: Based on the N1 time window, it is triggered only when the structural change behavior is confirmed within the sliding window;

[0110] Spatial index path: The composite spatial index uses the structure path partition and the binding template path to locate the mapping between specific fields and templates;

[0111] Semantic judgment labels: The judgment labels are based on interface splicing failure, field value null, and field semantic misalignment, which are used to identify structure-template mapping conflicts.

[0112] The above rule set is then attached to the dynamic pre-verification channel of the configurator for configuration pre-verification before interface registration.

[0113] Finally, before each dynamic tag configuration execution, the set of structurally unstable fields is mapped to the interface configuration template structure based on its corresponding structural drift coupling partition and pattern classification results, forming a list of structurally sensitive areas. At this point, the configurator uses N1 as a sliding analysis window and performs the following pre-registration checks on the fields in this list:

[0114] Check if the path to the bound field has been broken;

[0115] Are the field types consistent with the configuration template declaration?

[0116] Does the field pose a risk of being updated by name but not synchronized?

[0117] Furthermore, to improve semantic fault tolerance and enhance adaptability to typical drift scenarios, structural adaptation rule sets are established for the aforementioned typical configuration error scenarios, constructing three types of structural adaptation rule sets, which are then integrated into the pre-validation stage of the template parser:

[0118] Type drift adaptation rules: Call the field type evolution trajectory comparison module, combine the type change events recorded in the field in the structural change path, construct a type backtracking chain, which is used to deduce all acceptable types of the field, and generate a set of compatibility templates to ensure that the templates can still be registered under field type drift.

[0119] Naming drift adaptation rules: Call the field naming mapping table, compare the historical aliases of the fields with the semantic identifiers, and synchronize the field semantics to the template semantic index tree to achieve semantic binding fault tolerance resolution after the field name changes.

[0120] Path drift adaptation rules: Call the path attribution analyzer and path similarity scoring function to identify the broken chain behavior of field paths, and search for similar path nodes in the structural path graph. Through the path mapping redirection mechanism, automatically replace field path nodes during the template registration stage to realize path restoration binding.

[0121] Furthermore, after verifying the interface configuration parameters, pre-validating the field structure, and blocking configuration error risks, the interface registration information is formally written into the persistent interface registry, and the interface code generation engine is invoked to start the automated generation process. This process uses interface tags, request paths, HTTP methods, parameter structures, return field mappings, data source tags, and SQL operation definitions as core inputs to generate complete interface processing logic and register it with the routing system, enabling dynamic deployment without restarting.

[0122] Specifically, the interface code generator first parses the structured interface definition object and calls an internal Freemarker or custom DSL template engine to render the controller source code. The generated controller processing logic explicitly includes seven functional components: request parameter validation logic, data source context setting logic, SQL execution channel, result builder, pagination processor, cache controller, and response body builder.

[0123] After the generated controller source code undergoes integrity verification, it will enter the bytecode generation stage. The JDK API is prioritized for direct compilation into .class bytecode files. If the interface involves complex structures or depends on compilation performance, bytecode manipulation libraries such as ASM or Javassist can be used to construct the bytecode instruction tree in a structured manner, directly generating Class instance objects and loading them into the current class loader.

[0124] After successful compilation and class loading, the controller class registers the generated controller instance to the Spring MVC route mapping table by calling the dynamic registration mechanism provided by Spring Framework. During the route registration process, a unique mapping path is automatically generated based on the interface tag and request path, and then bound to the method processing logic defined in the controller.

[0125] When the interface is executed, it first receives an HTTP request from the client and extracts the request path and method identifier using Spring MVC's request mapping mechanism, matching the corresponding interface tag. Upon successful matching, based on the interface definition associated with that tag, it extracts all declared parameters from the request parameters and performs the following operations using the built-in parameter parser:

[0126] Parameter type conversion: Based on the parameter data type declared in the interface configuration, call the type converter to convert the string parameter into the target type;

[0127] Non-empty and format validation: The system checks whether the parameter is required and calls the expression matcher for parameters with regular expression restrictions to ensure that the parameter content conforms to the input rules declared in the interface.

[0128] After all parameter validations pass, the data source context holder is invoked to bind the current thread context to the target data source tag based on the database tag specified in the interface configuration, using the ThreadLocal mechanism. This provides a thread-level basis for data source selection for subsequent operations. Subsequently, when the data source dynamic router obtains a database connection object, it automatically parses the tag set by the current thread context and retrieves the connection pool object matching that tag from the global connection pool mapping dictionary, thus achieving runtime dynamic routing of the data source connection.

[0129] During the SQL execution phase, the unified SQL builder is invoked based on the SQL statements declared in the interface definition to complete the statement concatenation. To prevent SQL injection risks, JDBC's pre-compilation mechanism is enabled for all string type parameters, and explicit data types and maximum length thresholds are set when binding parameters to ensure controlled parameter boundaries and type consistency.

[0130] Next, the unified SQL executor is invoked to submit the SQL statement to the bound database connection for execution, and the streaming result set extractor is enabled to iterate through and extract the query results row by row. During the extraction process, based on the return field structure and field data type declared in the interface configuration, field mapping and data format standardization are performed on each row of result data, and finally, it is encapsulated into a standard JSON structure and stored in the response cache.

[0131] If pagination is enabled on the API, pagination calculation logic is automatically injected. First, the total number of records is queried. Then, the offset is calculated based on the page number and page capacity in the request parameters, and the pagination clause is dynamically concatenated. The final pagination result is encapsulated in the form of a triple of current page number, total number of pages, and pagination data.

[0132] If the interface has response caching enabled, the cache container will be queried before execution to see if there is already a match using the parameter group as the key. If a match is found, the cached content will be returned directly, skipping the SQL execution. If no match is found, the response will be written to the cache container after it is constructed, and the automatic expiration time will be set according to the caching policy in the interface configuration. The caching mechanism supports enabling and disabling at the interface level.

[0133] When the interface configuration involves multiple data source tags, the queries are executed segment by segment in a predefined query order, as follows:

[0134] Before each SQL statement is executed, an explicit switch to the data source bound to that statement is called.

[0135] Obtain the corresponding connection from the connection pool and execute the SQL statement.

[0136] Cache the results of the subquery in an in-memory result buffer;

[0137] After all subquery segments are executed, the multi-source data fusion executor is invoked to execute and manipulate the result set. Specifically, a hash index is built based on the field mapping relationship table declared in the interface configuration, and a memory-level hash JOIN operation is performed using the primary field as the base key-value pair. After the JOIN is completed, the field structure of the merged result is validated to ensure that the number, order, and type of fields match the interface definition. Finally, the standardized result is encapsulated into the response body.

[0138] After the API request is completed, the **context cleaner** is immediately invoked to perform the following resource reclamation tasks:

[0139] Clear the data source tag bound to ThreadLocal;

[0140] Return the database connection handle to the connection pool;

[0141] Release the result cache and multi-source concatenation buffer in memory;

[0142] Clear temporary data objects and cache indexes within the request cycle to avoid cross-thread data leakage.

[0143] Furthermore, to enhance system stability and dynamic availability, the scheduler will periodically invoke the database structure collector to perform structure snapshot collection on all data sources registered in table_service. Collection commands include standard SQL or system table queries, and the collected structure data will be compared field-by-field with the cached structure.

[0144] If a new field is added, its type is changed, or its path is migrated, the configuration cache updater will be invoked to refresh the cache table and provide the latest structure prompts to the interface editor.

[0145] Additionally, when a developer modifies an interface definition item through the Web Configurator, the **Interface Republisher** is invoked to perform the following operations:

[0146] Cancel the controller routing mapping for the existing interface;

[0147] The code generator and class loader are invoked to regenerate and load the controller bytecode;

[0148] Call the route manager to register the new version of the interface to the SpringMVC route table;

[0149] The interface version manager assigns version numbers to new and old interface tags, allowing older interfaces to be retained until the set retention period expires.

[0150] This invention also proposes a cross-database REST interface implementation system based on tag-based dynamic configuration, such as... Figure 2 As shown, it includes: a multi-label data source construction module, an interface definition and trial operation verification module, a structure drift identification and mapping partition module, a controller generation and dynamic execution management module, and signal connections between the modules.

[0151] The multi-tag data source building module is mainly used to build a data source tag registration mechanism and load structured configuration files, and complete the initialization and mapping registration of the multi-tag connection pool.

[0152] The interface definition and trial run verification module is mainly used to provide an interface definition configuration channel, support multi-tag operation structure parsing and field structure consistency verification, and perform trial run verification.

[0153] The structural drift identification and mapping partitioning module is mainly used to construct response heterogeneous frequency coupling windows based on structural drift features and response behavior sequences, identify structurally unstable fields, and establish drift index mapping and partitioning classification structures.

[0154] The controller generation and dynamic execution management module is mainly used to automatically generate interface controller logic and register it to the routing mapping table, execute dynamic SQL, multi-source concatenation, cache control and interface cleanup processes, and support hot updates of structure snapshots and interface redistribution.

[0155] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0156] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0157] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and inventive constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0158] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0159] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0160] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for implementing a cross-database REST interface based on tag-based dynamic configuration, characterized in that: include: Build a data source tag registration mechanism and load a structured configuration file, and perform multi-tag connection pool initialization and mapping registration; It provides an interface definition configuration channel, supports multi-tag operation structure parsing and field structure consistency verification, and performs trial run verification; Based on structural drift features and response behavior sequences, a response heterogeneous coupling window is constructed to identify structurally unstable fields and establish a drift index mapping and partition classification structure. The method involves constructing a response heterogeneous coupling window based on structural drift features and response behavior sequences, identifying structurally unstable fields, and establishing a drift index mapping and partitioning classification structure. Specifically, this includes: performing structural attribution modeling and dynamic feature extraction on the fields bound in the interface; statistically analyzing the data table hierarchy depth, field update time fluctuation amplitude, field hit rate variability, and field response stability for each field; constructing a field call cycle response spectrum based on hit rate variability and response stability; identifying frequency jump cycle segments; and calculating the response drift error frequency by combining the structural update timestamp. ,in, , This represents the minimum time difference between the structural update time and the response trough. The total duration of the sliding time window; the mean of the path overlap and flattening depth distribution of the calculated field in the multi-label template, combined with the data table hierarchy depth, generates the structural penetration coefficient. ,in, , This represents the average normalized depth of the field within the flattening template. The hierarchy depth of the field in the original data table. The length of the field path in the structural snapshot is defined; fields that exceed the set structural penetration threshold Λ0 and whose response drift error frequency exceeds the structural update interference threshold λ0 in two or more frequency jump period segments are selected to form the structural heterofrequency dominant field set; least common period extraction and wave energy spectrum analysis are performed on the structural heterofrequency dominant field set to extract the concentrated wave energy segment and determine the duration of the response heterofrequency coupling window accordingly; It automatically generates interface controller logic and registers it to the routing mapping table, executes dynamic SQL, multi-source concatenation, cache control and interface cleanup processes, and supports hot updates of structure snapshots and interface redistribution.

2. The method for implementing a cross-database REST interface based on tag-based dynamic configuration according to claim 1, characterized in that: Load multiple data source tags defined in the configuration file, establish the mapping relationship between tags and connection pools, provide an interface definition configuration channel, fill in the interface tags, paths, methods, parameters, return fields and operation content, and perform structure validation and parameter validation after submission, and perform trial run verification and structure result comparison.

3. The method for implementing a cross-database REST interface based on tag-based dynamic configuration according to claim 1, characterized in that: Using the duration of the response frequency coupling window as a fixed sliding analysis period, behavioral regression and frequency analysis windows are constructed. Multi-window linear fitting is performed on the field hit rate and response duration sequence. Fields with structural dynamic drift tendency are identified by combining the fitting residuals and fluctuation characteristics and included in the structurally unstable field set.

4. The method for implementing a cross-database REST interface based on tag-based dynamic configuration according to claim 3, characterized in that: Construct a triplet mapping relationship between field binding path, structure snapshot version and interface template location to form the field structure evolution trajectory and horizontal comparison view, extract features such as field response time lag, value vacancy rate and type matching error, and identify drift gap areas; We construct a structural drift coupling graph with field triples as the core, depict the path change and type migration process of fields between structural snapshot versions, and classify structural drift patterns into three types: field type replacement drift, field name replacement drift, and field path sinking drift.

5. The method for implementing a cross-database REST interface based on tag-based dynamic configuration according to claim 4, characterized in that: Map structurally unstable fields to the interface configuration context, identify three typical configuration error scenarios: field type drift, naming drift, and path migration, establish a two-dimensional structural drift index matrix, and divide structural drift coupling partitions. Extract the field structure change path and determine the corresponding drift mode. Construct a field semantic misalignment probability model within the response frequency coupling window, generate a typical configuration error scenario matching rule set and load it into the pre-verification channel. Perform path, type and naming consistency verification on structure-sensitive fields, and construct three types of structure adaptation rules to achieve configuration fault tolerance adaptation.

6. The method for implementing a cross-database REST interface based on tag-based dynamic configuration according to claim 5, characterized in that; After completing the interface configuration parameter verification and structure pre-verification, the interface automated generation process is executed to generate controller source code based on interface tags, parameter structure, data source tags and SQL operation definitions and register it to the routing mapping table; During the interface execution process, the system performs parameter extraction and validation, data source binding and dynamic routing, SQL construction and execution, result extraction and field mapping, multi-source concatenation and field structure validation, and reclaims context resources after the request ends. The scheduler periodically collects snapshots of the data source structure, and the interface redeployer supports uninterrupted redeployment and version management after changes to interface definition items.

7. A system implementing a cross-database REST interface based on tag-based dynamic configuration, characterized in that, include: The module includes a multi-label data source construction module, an interface definition and trial operation verification module, a structure drift identification and mapping partition module, a controller generation and dynamic execution management module, and signal connections between modules. The multi-tag data source building module is mainly used to build a data source tag registration mechanism and load a structured configuration file, and complete the initialization and mapping registration of the multi-tag connection pool; The interface definition and trial operation verification module is mainly used to provide an interface definition configuration channel, support multi-tag operation structure parsing and field structure consistency verification, and perform trial operation verification. The structural drift identification and mapping partitioning module is mainly used to construct a response heterogeneous coupling window based on structural drift features and response behavior sequences, identify structurally unstable fields, and establish a drift index mapping and partitioning classification structure. The structure drift identification and mapping partitioning module is specifically used to: perform structure attribution modeling and dynamic feature extraction on the fields bound in the interface, and to statistically analyze the data table level depth, field update time fluctuation, field hit rate variability and field response stability of each field. Based on the hit rate variability and response stability, a field call cycle response spectrum is constructed to identify frequency jump cycle segments, and the response drift error frequency is calculated by combining the structure update timestamp. ;in, , This represents the minimum time difference between the structural update time and the response trough. The total duration of the sliding time window; the mean of the path overlap and flattening depth distribution of the calculated field in the multi-label template, combined with the data table hierarchy depth, generates the structural penetration coefficient. ;in, , This represents the average normalized depth of the field within the flattening template. The hierarchy depth of the field in the original data table. The length of the field path in the structural snapshot is defined; fields that exceed the set structural penetration threshold Λ0 and whose response drift error frequency exceeds the structural update interference threshold λ0 in two or more frequency jump period segments are selected to form the structural heterofrequency dominant field set; least common period extraction and wave energy spectrum analysis are performed on the structural heterofrequency dominant field set to extract the concentrated wave energy segment and determine the duration of the response heterofrequency coupling window accordingly; The controller generation and dynamic execution management module is mainly used to automatically generate interface controller logic and register it to the routing mapping table, execute dynamic SQL, multi-source concatenation, cache control and interface cleanup processes, and support hot updates of structure snapshots and interface redistribution.

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