Cross-database Rest interface implementation method and system based on tagging dynamic configuration

Abstract

The invention 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 difficult structure change adaptation, difficult data source dynamic binding and complex multi-source query fusion. By defining a mapping relation of an interface label, a structure snapshot and a field triple, a field with an unstable structure is identified, and a structure drift coupling map is constructed, so that the dynamic adaptation of the structure is realized. The system supports cache setting and paging according to interface granularity, supports a structure snapshot regular acquisition and version management mechanism, and ensures interface availability and consistency in a structure change process. The method has the capabilities of deployment automation, structure fault tolerance and dynamic scheduling execution, and is suitable for a REST interface management system in a multi-source heterogeneous database fusion scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of interface dynamic deployment and structure configuration management, and more particularly, relates to a cross-database Rest interface implementation method and system based on tag-based dynamic configuration. BACKGROUND

[0002] In the existing multi-source data management system, the REST interface as the main way of data interaction usually relies on static structure definition and manual deployment, which is difficult to meet the actual needs of frequent structure changes and dynamic binding of multiple databases. Especially in large platforms, different data sources have significant differences in field definition, path ownership, naming rules and data types, etc. The traditional static configuration mechanism cannot effectively respond to structure drift. If the interface binding field is not adapted in time after the structure changes, it is easy to cause problems such as field value empty, response time abnormal and type mismatch, and even lead to interface failure or data misdirection.

[0003] In addition, most of the existing interface construction mechanisms lack automation capabilities, and developers need to manually write controller logic, bind routing paths and specify SQL statements, which cannot support high-frequency change and dynamic deployment scenarios. For multi-source scenarios that need to extract data from multiple databases, the existing solutions generally lack a unified data fusion mechanism, resulting in frequent problems such as repeated queries, structure conflicts and inconsistent responses. The structure drift detection mechanism mostly stays in low-frequency version difference comparison, without introducing real-time field behavior monitoring and response exception analysis, making it difficult to capture the semantic shift of fields in the running period.

[0004] In view of the above problems, the present application provides a solution. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a cross-database Rest interface implementation method and system based on tag-based dynamic configuration to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purposes, the present application provides the following technical solutions: In a preferred embodiment, it comprises: A data source tag registration mechanism is constructed and a structured configuration file is loaded, and a multi-tag connection pool initialization and mapping registration is performed; An interface definition configuration channel is provided, supporting multi-tag operation structure parsing and field structure consistency verification, and performing trial operation verification; A response abnormal frequency coupling window is constructed based on structure drift characteristics and response behavior sequence, and unstable fields are identified and drift index mapping and partition classification structure are established; Automatically generate interface controller logic and register it to the routing mapping table, execute dynamic SQL, multi-source splicing, cache control and interface cleanup processes, and support hot updates of structure snapshots and interface re-release.

[0007] In a preferred embodiment, multiple data source tag items defined in the configuration file are loaded, a mapping relationship between the tag and the connection pool is established, an interface definition configuration channel is provided, the interface tag, path, method, parameter, return field and operation content are filled in, and after submission, structure verification and parameter verification are performed, and a trial run verification and structure result comparison are performed.

[0008] In a preferred embodiment, structural attribution modeling and dynamic feature extraction are performed on the bound fields in the interface, and statistics are collected on the data table level depth, field update time fluctuation range, field hit rate variability and field response stability of each field; Based on the hit rate variability and response stability, a field call period response spectrum is constructed to identify the frequency jump period segment, and the response drift error frequency is calculated in combination with the structure update timestamp.

[0009] In a preferred embodiment, the path overlap and flattening depth distribution mean of the fields in the multi-label template are calculated, and the structural penetration coefficient is generated in combination with the data table level depth. The fields that meet the structural penetration threshold and the response drift error frequency criterion are screened out to form the structural heterofrequency dominant field set. The minimum common period multiplication extraction and fluctuation energy spectrum analysis are performed on the set of structural heterofrequency dominant fields to extract the concentrated fragments of fluctuation energy, and the duration of the response heterofrequency coupling window is determined accordingly.

[0010] In a preferred embodiment, the duration of the response heterofrequency coupling window is used as a fixed sliding analysis period, and a behavioral regression and frequency analysis window is constructed. Multi-window linear fitting is performed on the field hit rate and response duration sequence, and the fitting residuals and fluctuation characteristics are combined to calibrate the fields with a tendency of structural dynamic drift and include them in the set of structural unstable fields.

[0011] In a preferred embodiment, a triple mapping relationship between field binding path, structure snapshot version and interface template position is constructed to form a field structure evolution trajectory and a horizontal comparison view, extract features such as field response time lag, value vacancy rate and type matching error, and identify drift crack areas; A structural drift coupling graph is constructed with field triples as the core to characterize the path change and type migration process of fields between structural snapshot versions, and the structural drift patterns are classified into three types: field type replacement drift, field naming replacement drift, and field path sinking drift.

[0012] In a preferred embodiment, structurally unstable fields are mapped to interface configuration contexts, three typical configuration error scenarios, namely 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; The field structure change path is extracted and the corresponding drift pattern is determined. A field semantic misalignment probability model is constructed within the response heterogeneous coupling window. A typical configuration error scenario matching rule set is generated and loaded into the pre-verification channel. Path, type and naming consistency verification is performed on structure-sensitive fields, and three types of structure adaptation rules are constructed to achieve configuration fault-tolerant adaptation.

[0013] In a preferred embodiment, after completing the interface configuration parameter verification and structure pre-verification, the interface automatic generation process is executed to generate the controller source code based on the interface label, parameter structure, data source label and SQL operation definition and register it to the routing mapping table; During the interface execution process, parameter extraction and verification, data source binding and dynamic routing, SQL construction and execution, result extraction and field mapping, multi-source splicing and field structure verification are performed, and context resources are recycled after the request ends; the scheduler regularly collects data source structure snapshots, and the interface re-publisher supports non-interruption redeployment and version management after changes to interface definition items.

[0014] 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; The multi-label data source construction module is mainly used to build a data source label registration mechanism and load structured configuration files to complete the multi-label connection pool initialization and mapping registration; 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 partition module is mainly used to construct a response heterofrequency coupling window based on the structural drift characteristics and response behavior sequence, identify structural instability fields and establish a drift index mapping and partition classification structure; 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 splicing, cache control and interface cleanup processes, and support hot updates of structure snapshots and interface re-release.

[0015] The technical effects and advantages of the cross-database REST interface implementation method and system based on tag-based dynamic configuration of the present invention are as follows: The present invention effectively identifies structurally unstable fields and their drift crack areas by defining structural snapshots, field triple mappings, and structural drift coupling maps, thereby achieving dynamic structural adaptation. Labeled configuration and template parsing methods are used to achieve automatic mapping of field behaviors in interface definitions, significantly improving adaptability under structural changes. Through an interface code generator and a dynamic routing registration mechanism, the automatic generation and hot deployment of interface logic are supported, reducing development workload and supporting uninterrupted updates of interfaces. Runtime data source context binding and dynamic routing mechanisms are introduced to ensure that interfaces can dynamically switch target database connections based on labels, adapting to multi-source heterogeneous scenarios. Through a unified SQL executor and multi-source data fusion logic, structural consistency and semantic unification of results across data sources are achieved. Interface responses support parameter verification, paging processing, and caching strategies, further improving interface execution efficiency and stability. A structure collector and interface republisher construct an interface hot update and version management mechanism to ensure stable system operation under structural changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a cross-database REST interface implementation method and system sequence diagram based on label-based dynamic configuration of the present invention.

[0017] Figure 2 This is a schematic diagram of the cross-database REST interface implementation method and system modules based on label-based dynamic configuration of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] Embodiment: The present invention discloses a cross-database REST interface implementation method based on tagging dynamic configuration, such as Figure 1 Shown, including: Build a data source tag registration mechanism and load a structured configuration file to complete multi-tag connection pool initialization and mapping registration.

[0020] Provides interface definition configuration channels, supports multi-tag operation structure parsing and field structure consistency verification, and performs trial run verification.

[0021] Based on the structural drift characteristics and response behavior sequence, a response heterofrequency coupling window is constructed to identify the structural unstable fields and establish a drift index mapping and partition classification structure.

[0022] Automatically generate interface controller logic and register to the routing mapping table, execute dynamic SQL, multi-source splicing, cache control and interface cleaning process, support structure snapshot hot update and interface re-release.

[0023] In the initialization and interface configuration phase, to achieve cross-database label dynamic binding, first load the configuration file supporting multi-format configuration through the deployment phase, which includes local YAML file, JSON file or remote configuration center service node.

[0024] Each item in the configuration file defines all available data source label items, each of which explicitly includes the data source label name, the corresponding database type, the JDBC driver class fully qualified name, the database connection URL, the authentication username and password, and the connection pool related core parameters such as the minimum idle connection number, the maximum active connection number, and the connection timeout time. The source of the above configuration items depends on the agreed format of the deployment file, which ensures that the data structure is verified for field integrity and format legality by the configuration validator before loading.

[0025] In the application startup phase, according to the content of the above configuration file, call the configuration parser implemented based on the SpringBoot configuration binding mechanism extension to map each data source label item to a structured configuration entity object. Then, call the factory method of the connection pool constructor HikariCP to generate the corresponding physical connection pool object for each structured configuration entity, and execute the connection pool initialization logic, including driver loading, connection warming up and connection pool parameter injection.

[0026] After the construction is completed, establish a binding mapping relationship between each data source label and its corresponding connection pool object, and register it to the runtime data source mapping dictionary; The mapping dictionary uses the thread-safe ConcurrentHashMap structure, taking the label name as the key and the connection pool object as the value, to ensure access consistency in a multi-threaded environment.

[0027] After completing the data source label registration, provide a REST interface definition configuration channel as the developer interface registration entry. Users fill in the interface definition form through the front-end Web configurator, which specifies the unique REST interface label, interface request path, HTTP request method, parameter name and type definition, return field format specification, whether to enable the paging mechanism, whether to enable response caching, and the interface bound data source label and specific operation definition.

[0028] Operation definition supports three types of operation modes: The first is the native SQL statement, which is written directly in complete text form; Second, dynamic SQL statements with placeholders support subsequent parameter injection; Third, multi-data source combined operation configuration supports subquery or JOIN operation under multiple tags, and each paragraph is bound to different tags.

[0029] Then, after the user submits the interface configuration item, the structure verifier first verifies the structure consistency of the interface configuration content. The structure verifier 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 missing, redundant, or type inconsistency problems, the structure verification error prompt is returned immediately.

[0030] After structure verification, the data source router is called to locate the corresponding connection pool object according to the data source tag bound in the interface configuration, and a test SQL statement with test values is constructed. Then, the unified SQL executor calls the connection pool to execute the test query, and the returned result structure is verified by the returned structure verifier for field structure and type alignment. If the test run result does not meet the return structure defined in the interface configuration, the system will return this error as configuration phase blocking information to the development end, prompting the developer to modify the configuration until the structure definition and the running result are consistent.

[0031] It should be noted that when multiple tags are bound and field template parsing is performed during the REST interface configuration phase, if the database corresponding to a tag has undergone non-destructive structural changes such as field type modification, field name renaming, or field desensitization strategy adjustment, such as replacing an integer field with an encrypted string, a plaintext field with a masked field, it will cause the original field-parameter mapping template in the interface registry to deviate from the actual structure of the current field. Since the field structure snapshot in the initial interface configuration cache is not bound to the historical structure version of the tag field, the configuration will use the cached field template for parameter generation and interface registration in the running phase, resulting in field mapping drift.

[0032] Further leading to parameter type conflict, field value format incompatibility, or query structure mismatch, which may cause interface execution interruption and throw syntax exceptions, or return a structure misplaced dataset under the illusion of successful interface running, causing logical errors in query results that are difficult to detect. Especially in interface configurations involving multiple tags nested subqueries or combined queries, the field structure originally bound to each tag may be located in different databases, and different fields may change asynchronously due to version updates.

[0033] In the current solution, the field parameter template only maintains a field snapshot once, and the field parameter parser flattens the field definitions of multiple tags into a unified template during the merging phase, ignoring the differences in the structural origins of each tag field itself. This results in mixed structures, field name conflicts, and type normalization errors in the final generated field binding template, making it impossible to correctly distinguish field semantics during runtime, ultimately leading to field value mis-parsing, cross-tag field value mis-binding, and query logic misalignment. Therefore, in this embodiment, before executing dynamic tag configuration, in order to identify the stability issues of field parameter binding under potential structural evolution, the tag call history and field response duration sequence recorded in the daily operation log are first used as the original sample set, and structural attribution modeling and dynamic feature extraction are performed on all fields bound to tags in the current interface.

[0034] Specifically, for each called field in the interface, the field binding path in each tag configuration template is parsed. Based on the data table hierarchy pointed to by the path, a structural attribution sequence for the field is constructed. This structural attribution sequence is represented by a path stack of the field in the database logical structure. The data table hierarchy depth is extracted by the structure snapshot collector through the system table.

[0035] After the structure attribution is determined, the following four types of dynamic indicator extraction are performed for each field path: Data table level depth: calculated by the number of table level nodes appearing in the field path, directly mapping the physical structure nesting depth of the field; 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 update frequency standard deviation to reflect the degree of instability of the structure evolution; Field hit rate variability: In the daily API call history, we count the number of field calls using a sliding window and calculate the standard deviation within the period to identify the fluctuation of its usage frequency. Field response stability: Based on the field response time series, local variance calculation is performed within a fixed window, and the presence of high-frequency mutation points is identified to determine the degree of fluctuation of the field response behavior.

[0036] Next, using field hit rate variability and field response stability as dual core variables, a field call cycle response spectrum was constructed. This spectrum plots the behavior of each field over multiple consecutive call cycles, with time as the horizontal axis and hit rate and response duration as the vertical axes. A periodic sliding difference algorithm was used to identify time windows in the spectrum where the hit rate continuously decreases and the response duration exhibits periodic sawtooth oscillations. These windows are marked as frequency hopping periods.

[0037] Subsequently, the structural update timestamp recorded in the aforementioned field update time fluctuation amplitude is combined to align the structural update time point to the frequency jump period, and the time offset ratio between the structural update time point and the field response trough is calculated. This time offset ratio is defined as the response drift error frequency, which is used to measure the timing coupling strength between the field structure evolution behavior and the response instability during operation. The statistics of this time offset ratio are based on the timestamp alignment mechanism between the structural update log and the response behavior log, ensuring that the error frequency determination has a clear data source.

[0038] The response drift error frequency is defined as: η(t)=Δt_drift / T_window; Where Δt_drift is the minimum time difference between the structural 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 in the field response behavior.

[0039] Next, for each field, the degree of path overlap that is commonly referenced in multiple label configurations is calculated. Specifically, the tail paragraph of the field's binding path in each template is used as the basis for comparison, the proportion of its path consistency is counted, and a binding overlap index is generated. At the same time, combined with the normalized depth value of the field in the flattened template, that is, the flattened index position of the field in the unified template, the mean of the flattened depth distribution in all label templates is calculated, and combined with the original data table level depth to generate the field structure penetration coefficient; The field structure penetration coefficient is defined as: Λ(t,s)=(D_flat(t,s) / D_raw(t,s))×log(1+L_path(t,s)); Where D_flat(t,s) is the average normalized depth of the field in the flattened 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 field parsing complexity.

[0040] 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 hopping periods, the field is included in the structural frequency-dominant field set. This structural frequency-dominant field set represents a subset of fields with deep structural path penetration and significant frequency-error response behavior, representing key objects where structural evolution affects response stability.

[0041] The recommended value range of the structure penetration threshold Λ0 is 2.5-3.5, and the specific value is determined by the mean value plus two standard deviations of the historical sample distribution of the structure penetration coefficient. The value of the response drift frequency λ0 is set to 0.25, which represents that the time offset ratio between the structure update time and the response anomaly needs to account for more than 25% of the cycle length to be considered to have a significant frequency correlation. The above threshold values are obtained from the statistical analysis of the operation logs of 100 typical multi-label interfaces within three months.

[0042] In the structure frequency dominant field set, the minimum common period extraction is performed on the frequency jump period sequence of each field. This process uses the period integer sequence intersection method to obtain the synchronous rhythm of the behavior response of multiple fields, and extracts the minimum common period value as the candidate analysis window period. Subsequently, the response value fluctuation bandwidth of each field within the candidate period segment, i.e., the difference between the maximum and minimum response values, is taken as the input, and the fluctuation energy spectrum analysis is performed to calculate the fluctuation energy concentration, and the period segment with high energy concentration is selected according to the set high energy interval threshold, which is defined as the fluctuation energy concentration segment.

[0043] Then, the minimum continuous time window length is extracted from the fluctuation energy concentration segment, and the length is defined as the duration of the response frequency coupling window.

[0044] It should be noted that the window time scale of the final sliding analysis has the behavior frequency driving characteristic in the structure penetration background, which can accurately capture the field response instability process triggered by structure evolution, and provides time domain support for dynamic identification of structure unstable field set.

[0045] Subsequently, the duration of the response frequency coupling window is defined as N1, and the sliding analysis window is constructed with a fixed time interval N1. In each window period, the multi-window linear regression fitting is performed on the hit rate sequence and the response time sequence of the field, and the fluctuation frequency analysis is performed based on the fitting residual and local fluctuation variance.

[0046] In this analysis process, the field response sequence fitting module is first called to construct a linear fitting curve for the field in the time-response space using the behavior sequence in the sliding window. Then, the correlation coefficient and the fitting slope change rate are calculated to construct a correlation determination function.

[0047] At the same time, an improved periodic fluctuation analysis algorithm is used to extract the first derivative change period and the peak-valley density of the response value in each sliding window to generate a fluctuation threshold function to determine whether the response behavior has a structure dominant high-frequency disturbance.

[0048] When the fitting correlation coefficient of a field continues to decrease, the fluctuation amplitude and frequency continue to increase in multiple sliding window periods, and the fitting curve shows the characteristics of inflection point accumulation or slope oscillation, the field is marked as a field with a significant structural dynamic drift tendency and is included in the set of structurally unstable fields; This process forms a dynamic identification mechanism for structural stability, supported by behavioral fitting trends and spanning a time window. The size of the sliding window used to perform the above analysis is defined as the label drift determination window.

[0049] Furthermore, after identifying the set of structurally unstable fields, we first construct a triple mapping relationship between the label field, the structural snapshot version, and the interface template position based on the field binding path and its binding position in the interface template. Then, by analyzing the difference in the relationship between the physical path of the field in the structural snapshot and its template mapping position, we establish the evolution trajectory of the field under different structural versions and form a horizontal comparison view of the field structure parsing behavior.

[0050] On this basis, we further extract three types of abnormal features related to field semantic offset: Field response time lag: This refers to the delay period in which the response time increases significantly after the field structure is updated. Field value vacancy rate: the percentage of null or missing values ​​in the returned results; Type matching error: The difference between the field return value type and the expected type of the interface template, including format incompatibility and type deduction failure.

[0051] Through cross-analysis of the above three types of indicators, we can identify the mapping gaps between field structure evolution and template parsing, which are called drift gap areas. These drift gap areas are high-risk sections where structural changes are not synchronously recognized by the template, resulting in field semantics shifts during runtime.

[0052] Subsequently, using the aforementioned field triples as the core of the analysis, a structural drift coupling graph was constructed. This graph uses fields as nodes, structural snapshot versions as state edges, and interface template behaviors as attached labels. It depicts the path changes, type migrations, and naming transformations of fields during structural evolution. In this graph, structural drift patterns are categorized into three main types based on the type of field change event and the scope of structural semantic impact: Field type replacement drift: The marked field type in the structure snapshot is replaced from a numeric type to a masked character type or an encrypted type. This causes inconsistencies between the original field definition in the interface template and the actual field type. Type deduction fails, causing the field to be automatically set to blank or have a default value inserted during the splicing process, disrupting the field splicing order. Field name replacement drift: The structure log records changes to field names or aliases, but the interface template cache is not refreshed synchronously. The old field names are still used during query construction, causing the fields to fail to match the structure. The returned results may contain missing fields or mismatched fields, resulting in semantic shifts without error reporting. Field path drift: After the physical structure of a field is migrated to a child table, split table, or associated table, the interface template is still bound to the old path field. During parsing, the target field cannot be found and the field is mistakenly bound to a field with a similar structure or returned as empty. This ultimately causes contextual misalignment or semantic confusion in the field value.

[0053] Based on the above-mentioned structural drift map, we further mapped the identified structurally unstable fields to specific interface configuration contexts. Based on the correspondence between field ownership paths, field change types, and interface splicing behaviors, we constructed the following three typical configuration error scenarios: Scenario 1: Field type drift causes splicing errors: In a multi-label merging configuration, a field was originally an integer and later updated to a masked string. The interface merge template still splices according to the original type, causing type deduction failure and overflow or truncation of the splicing length, resulting in data structure expansion or field loss. Scenario 2: Field name drift leads to hidden field loss: the field alias is updated but the template cache is not refreshed. The interface still calls the old field name, and the query returns a null value without an error. The result structure is complete, but the field meaning is incorrect, which is difficult to detect. Scenario 3: Field path migration causes value binding offset: The field is migrated from the main table to the child table, and the interface template does not complete the path redirection. After the binding fails, the default upward matching is performed on fields with similar structures, resulting in incorrect field value references or contextual semantic misalignment.

[0054] Furthermore, the identified structurally unstable field sets are mapped to a two-dimensional structural drift mapping area. This mapping area uses field attribution path information and response behavior sequence characteristics as joint index dimensions to ensure synchronous modeling of structure and behavior, as follows: Field-attribution path dimension: Reads the field's binding path in the label template structure, uses the path resolver to parse the field's path stack into a structure-level depth sequence, and partitions it based on the path prefix to form a path partition index space. Response behavior dimension: Within the N1 time window, the perturbation map builder is used to generate a field response frequency perturbation map, combining the two characteristic dimensions of response drift error frequency and fluctuation energy concentration. This map uses time as the horizontal axis and energy concentration as the vertical axis to map the correlation between the field's response stability and structural evolution.

[0055] The two dimensions are jointly indexed to construct a two-dimensional structural drift index matrix. In this structural drift index matrix, the minimum structural penetration depth of a field in the path partition is used as the primary index dimension, indicating the deepest level at which the field is referenced during the structural flattening process. The periodic perturbation characteristic frequency extracted from the response perturbation map is used as the secondary index dimension, marking the intensity and frequency of periodic frequency errors of the field at the behavioral level.

[0056] Subsequently, a multidimensional sparse clustering analysis was performed on the structural drift index matrix, adaptively clustering the field feature points within the matrix using a sparse K-means-S variant algorithm. The clustering objective function combines structural depth compactness with response perturbation homogeneity, ultimately identifying several structural-response coupling clusters. Within each coupling cluster, the fields share similar nested structural paths and convergent response frequency offset characteristics. The coupling clusters are then defined as structural drift coupling partitions, serving as the minimum unit for subsequent classification and scenario matching.

[0057] The number of clusters k is determined dynamically by the silhouette coefficient analysis method, which calculates the average silhouette coefficient corresponding to all possible k values ​​and selects the optimal value. The calculation of the silhouette coefficient S(i) follows: S(i)=(b(i)-a(i)) / max{a(i),b(i)}; 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 cluster. If the maximum average silhouette coefficient is less than 0.5, the data is considered unsuitable for clustering. The vectors of field feature points are normalized using the Z-score before clustering to ensure dimensionality balance.

[0058] Furthermore, within each structural drift coupling partition, the historical structural version trajectory of the field is extracted. This trajectory comes from the cross-version structural change records of the field in the structural snapshot collector. Combined with the field registration snapshots in the interface template cache, the actual binding record of the field along the structural evolution path is constructed, thus generating a sequence of field structural change paths. Then, pattern recognition is performed on the structural variation behavior in the path sequence. Based on whether the change path involves field type replacement, field name renaming, or field path migration link disconnection, the following three types of structural drift pattern judgments are performed: TypeReplace mode: If a field type is changed from a numeric type to a masked character type or an encrypted type in a structure snapshot, the binding template is not updated synchronously. This type incompatibility causes splicing failure or a vacant field. NameReplace mode: When a field name or alias is changed at the database structure level, the template is not refreshed synchronously, and the field name cannot match the template, resulting in missing or misplaced fields. PathSink mode: When a field is migrated to a child table, split into a table, or the path is changed, the interface template is still bound to the old path. If field matching fails, the binding falls back to similar fields, resulting in semantic mismatch.

[0059] Furthermore, to ensure that mode judgment has time boundary constraints and behavior-driven characteristics, the structural evolution behavior is sampled and judged only within the response heterogeneous coupling window defined by N1, ensuring that all classification logics are only valid when response anomalies occur, thus avoiding misjudgment of structural drift.

[0060] After determining the structural drift pattern, the structural risk modeler is invoked, based on the structural drift coupling partition, to construct a field structure semantic misalignment probability model. This model uses the structural change path type, response volatility, field vacancy rate, and historical binding mismatch frequency as input variables to calculate the probability of a field experiencing semantic drift in the current template.

[0061] On this basis, we further define a set of matching rules for typical configuration error scenarios. Each rule consists of the following three layers: Time trigger condition: Based on the N1 time window, it is triggered only when the structural change behavior is confirmed within the sliding window; Spatial index path: Use the structure path partition and the bound template path as the joint spatial index to locate the specific field and template mapping; Semantic judgment labels: Interface splicing failure, empty field value, and field semantic misalignment are used as judgment labels to identify structure-template mapping conflicts.

[0062] The above rule set is mounted to the dynamic pre-verification channel of the configurator to perform configuration pre-verification before interface registration.

[0063] Finally, before each tag dynamic configuration is executed, the structurally unstable field set is mapped to the interface to-be-configured template structure based on the structural drift coupling partition and pattern classification results to form a list of structurally sensitive areas. At this time, the configurator uses N1 as a sliding analysis window and performs the following pre-registration checks on the fields in the list: Whether the binding field path is broken; Whether the field type is consistent with the configuration template declaration; Whether the field has the risk of being updated without synchronization.

[0064] Furthermore, to improve semantic fault tolerance and enhance adaptability to typical drift scenarios, we establish structural adaptation rule sets for the above typical configuration error scenarios. We construct three types of structural adaptation rule sets and integrate them into the pre-verification stage of the template parser: Type drift adaptation rule: call the field type evolution track comparison module, combine the type change events recorded in the structure change path, construct a type backtracking chain, and generate a compatibility template set to ensure that the template can still be registered under field type drift.

[0065] Naming drift adaptation rule: call the field naming mapping table, compare the field history alias and semantic identifier, and synchronize the field semantics to the template semantic index tree to realize fault-tolerant parsing of semantic binding after field name change.

[0066] Path drift adaptation rule: call the path ownership analyzer and path similarity scoring function to identify the broken link behavior of the field path, and retrieve similar path nodes in the structure path graph. Through the path mapping redirection mechanism, automatically replace the field path node in the template registration stage to realize path recovery binding.

[0067] Further, after completing the interface configuration parameter verification, field structure pre-check and configuration error risk blocking, the interface registration information is formally written into the persistent interface registration table, and the interface code generation engine is called to start the automatic generation process. This process takes the interface label, request path, HTTP method, parameter structure, return field mapping, data source label and SQL operation definition as the core input to generate complete interface processing logic and register it to the routing system, realizing dynamic deployment without restarting.

[0068] Specifically, the interface code generator first parses the structured interface definition object and calls the internal Freemarker or custom DSL template engine rendering controller source code. In the generated controller processing logic, it explicitly includes seven functional components: request parameter verification logic, data source context setting logic, SQL execution channel, result constructor, paging processor, cache controller and response body constructor; The generated controller source code will enter the bytecode generation stage after integrity check. Preferably, the API provided by JDK is called to compile the source code directly into a.class bytecode file; if the interface involves complex structures or depends on compilation performance, you can choose to use bytecode manipulation libraries such as ASM or Javassist to construct bytecode instruction trees in a structured way, directly generate Class instance objects and load them into the current class loader.

[0069] After the controller class is successfully compiled and loaded, the generated controller instance is registered in the SpringMVC routing map table by calling the dynamic registration mechanism provided by SpringFramework. During the routing registration process, a unique mapping path is automatically generated based on the interface label and request path, and it is bound to the method processing logic defined in the controller.

[0070] When the interface is executed, it first receives the HTTP request from the client and uses SpringMVC's request mapping mechanism to extract the request path and method identifier, matching the corresponding interface tag. If a match is successful, all declared parameters are extracted from the request parameters based on the interface definition associated with the tag, and the built-in parameter parser is used to complete the following operations: 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; Non-empty and format validation: The system determines whether the parameters are required and calls the expression matcher for parameters with regular expression restrictions to ensure that the parameter content complies with the input rules declared in the interface.

[0071] After all parameter checks pass, the data source context holder is called to bind the current thread context to the target data source tag via the ThreadLocal mechanism based on the database tag specified in the interface configuration. This provides thread-level data source selection criteria for subsequent operations. Subsequently, when obtaining a database connection object, the data source dynamic router automatically parses the tag set in the current thread context and retrieves the connection pool object that matches the tag from the global connection pool mapping dictionary, implementing runtime dynamic routing of data source connections.

[0072] During the SQL execution phase, a unified SQL builder is called based on the SQL statements declared in the interface definition to complete statement splicing. To prevent SQL injection risks, JDBC's precompilation mechanism is enabled for all string type parameters. When binding parameters, explicit data types and maximum length thresholds are set to ensure parameter boundaries are controlled and type consistency is maintained.

[0073] The unified SQL executor is then called to submit the SQL statement to the bound database connection for execution. The streaming result set extractor is then enabled to extract the query results row by row. During the extraction process, field mapping and data format standardization are performed on each row of result data based on the return field structure and field data types declared in the interface configuration. The data is then encapsulated into a standard JSON structure and stored in the response buffer.

[0074] If paging is enabled on the interface, paging 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 paging clauses are dynamically spliced. The final paging result is encapsulated in the form of a triplet of the current page number, the total number of pages, and the paging data.

[0075] If the interface enables the response cache configuration, the cache container is queried with the parameter group as the key before execution to determine whether there is hit data; if there is a hit, the cache content is directly returned, and SQL execution is skipped; if there is no hit, the response is written into the cache container after being constructed, and an automatic expiration time is set according to the cache strategy in the interface configuration. The cache mechanism supports opening and closing by interface granularity.

[0076] When the interface configuration involves multiple data source tags, each segment is executed in a predefined query order as follows: Before each segment SQL execution, an explicit switch to the data source bound to the segment is called; A corresponding connection is obtained from the connection pool, and the segment SQL is executed; The subquery result is cached in the in-memory result buffer; After all subquery segments are executed, the multi-source data fusion executor is called to perform result set operation. Specifically, a hash index is constructed according to the field mapping relationship table declared in the interface configuration, and the primary field is used as the key value pair basis to perform in-memory level hash JOIN operation. After JOIN is completed, field structure verification is performed on the merged result to ensure that the field number, order and type are matched with the interface definition, and finally the standardized result is packaged into the response body.

[0077] After the interface request execution is completed, the **context cleaner is immediately called to complete the following resource recycling work: The data source tag bound in ThreadLocal is cleared; The database connection handle is returned to the connection pool; The result cache area and multi-source splicing buffer in memory are released; The temporary data objects and cache indexes in the request period are emptied to avoid cross-thread data leakage.

[0078] Further, to enhance system stability and dynamic availability, the scheduler will periodically call the database structure collector to perform structure snapshot collection on all data sources registered in table_service. The collection command includes standard SQL or system table query, and the collected structure data is compared with the cache structure field by field.

[0079] If a field is added, the type is changed, or the path is migrated, the configuration cache updater is called to refresh the cache table and provide the latest structure prompt to the interface editor.

[0080] At the same time, when the developer performs modification operations on the interface definition item through the Web configurator, the **interface republisher is called to perform the following operations: The controller route mapping of the original interface is revoked; The code generator and class loader are called to regenerate and load the controller bytecode; Call the routing manager to register the new version interface to the SpringMVC routing table; The interface version manager adds version numbers to the old and new interface tags, allowing the old interface to be retained until the set retention period expires.

[0081] The present invention also proposes a cross-database REST interface implementation system based on tagging 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 modules.

[0082] The multi-tag data source construction module is mainly used to build a data source tag registration mechanism and load structured configuration files to complete the multi-tag connection pool initialization and mapping registration.

[0083] The interface definition and trial operation verification module is mainly used to provide an interface definition configuration channel, support multi-label operation structure parsing and field structure consistency verification, and perform trial operation verification.

[0084] The structural drift identification and mapping partition module is mainly used to construct a response heterofrequency coupling window based on the structural drift characteristics and response behavior sequence, identify structural instability fields and establish a drift index mapping and partition classification structure.

[0085] 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 splicing, cache control and interface cleanup processes, and support hot updates of structure snapshots and interface re-release.

[0086] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0087] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0088] Those skilled in the art will appreciate that the modules and algorithm steps of each example 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 performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel 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.

[0089] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0090] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0091] Finally: 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 in the scope of protection of the present invention.

Claims

1. A cross-database REST interface implementation method based on tag-based dynamic configuration, characterized in that: include: Build a data source tag registration mechanism and load structured configuration files to perform multi-tag connection pool initialization and mapping registration; Provides an interface definition configuration channel, supports multi-tag operation structure parsing and field structure consistency verification, and performs trial operation verification; Based on the structural drift characteristics and response behavior sequence, a response hetero-frequency coupling window is constructed to identify the structural instability field and establish a drift index mapping and partition classification structure; Automatically generate interface controller logic and register it to the routing mapping table, execute dynamic SQL, multi-source splicing, cache control and interface cleanup processes, and support hot updates of structure snapshots and interface re-release.

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

3. The cross-database REST interface implementation method based on tag-based dynamic configuration according to claim 2 is characterized in that: Perform structural attribution modeling and dynamic feature extraction on the bound fields in the interface, and calculate the data table level depth, field update time fluctuation range, field hit rate variation, and field response stability of each field; Based on the hit rate variability and response stability, a field call period response spectrum is constructed to identify the frequency jump period segment, and the response drift error frequency is calculated in combination with the structure update timestamp.

4. The cross-database REST interface implementation method based on tag-based dynamic configuration according to claim 3, characterized in that: Calculate the path overlap and flattening depth distribution mean of the fields in the multi-label template, generate the structural penetration coefficient based on the data table level depth, and select the fields that meet the structural penetration threshold and response drift error frequency criteria to form the structural heterofrequency dominant field set; The minimum common period multiplication extraction and fluctuation energy spectrum analysis are performed on the set of structural heterofrequency dominant fields to extract the concentrated fragments of fluctuation energy, and the duration of the response heterofrequency coupling window is determined accordingly.

5. The cross-database REST interface implementation method based on tag-based dynamic configuration according to claim 4 is characterized in that: Taking the duration of the response heterofrequency coupling window as a fixed sliding analysis period, a behavioral regression and frequency analysis window is constructed, and a multi-window linear fitting is performed on the field hit rate and response duration series. The fitting residuals and fluctuation characteristics are combined to calibrate the fields with a tendency of structural dynamic drift and include them in the set of structural unstable fields.

6. The cross-database REST interface implementation method based on tag-based dynamic configuration according to claim 5 is characterized in that: Construct a triplet mapping relationship between field binding path, structure snapshot version, and interface template location to form a field structure evolution trajectory and horizontal comparison view. Extract features such as field response time lag, value vacancy rate, and type matching error to identify drift crack areas. A structural drift coupling graph is constructed with field triples as the core to characterize the path change and type migration process of fields between structural snapshot versions, and the structural drift patterns are classified into three types: field type replacement drift, field naming replacement drift, and field path sinking drift.

7. The cross-database REST interface implementation method based on tag-based dynamic configuration according to claim 6 is characterized in that: Mapping structurally unstable fields to interface configuration contexts, identifying three typical configuration error scenarios: field type drift, name drift, and path migration, establishing a two-dimensional structural drift index matrix, and dividing structural drift coupling partitions; The field structure change path is extracted and the corresponding drift pattern is determined. A field semantic misalignment probability model is constructed within the response heterogeneous coupling window. A typical configuration error scenario matching rule set is generated and loaded into the pre-verification channel. Path, type and naming consistency verification is performed on structure-sensitive fields, and three types of structure adaptation rules are constructed to achieve configuration fault-tolerant adaptation.

8. The cross-database REST interface implementation method based on tag-based dynamic configuration according to claim 7, characterized in that: After completing the interface configuration parameter verification and structure pre-verification, execute the interface automatic generation process, generate the controller source code based on the interface label, parameter structure, data source label and SQL operation definition, and register it to the routing mapping table; During the interface execution process, parameter extraction and verification, data source binding and dynamic routing, SQL construction and execution, result extraction and field mapping, multi-source splicing and field structure verification are performed, and context resources are recycled after the request ends; the scheduler regularly collects data source structure snapshots, and the interface re-publisher supports non-interruption redeployment and version management after changes to interface definition items.

9. A cross-database REST interface implementation system based on tag-based dynamic configuration, characterized by: include: Multi-label data source construction module, interface definition and trial operation verification module, structural drift identification and mapping partition module, controller generation and dynamic execution management module, and signal connection between modules; The multi-label data source construction module is mainly used to build a data source label registration mechanism and load structured configuration files to complete the multi-label connection pool initialization and mapping registration; 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 partition module is mainly used to construct a response heterofrequency coupling window based on the structural drift characteristics and response behavior sequence, identify structural instability fields and establish a drift index mapping and partition classification structure; 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 splicing, cache control and interface cleanup processes, and support hot updates of structure snapshots and interface re-release.

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