Java enterprise rapid development method based on visual modeling

Abstract

The application discloses a Java enterprise-level rapid development method based on visual modeling, which comprises the following steps: collecting modeling behaviors to generate cognitive graphs, constructing views and collecting operation behaviors to generate cognitive feature data; generating a semantic constraint model, fusing cognitive features and node features to construct the semantic constraint model; performing semantic consistency detection, automatically comparing design and implementation differences and triggering bidirectional correction; generating a project engineering structure, generating structured code and interface documents according to the corrected model; performing zero-downtime migration evolution, constructing a migration relationship graph and adopting a gray mode to realize seamless switching; and performing semantic self-correction and updating in the running period, monitoring semantic deviation and backfilling design content to realize closed-loop synchronization. The application realizes intelligent modeling and uninterrupted evolution of Java enterprise-level applications through a cognitive graph driving and multi-modal migration mechanism.

Description

Technical Field

[0001] This invention relates to the fields of software engineering and intelligent development technology, and in particular to a Java enterprise-level rapid development method based on visual modeling. Background Technology

[0002] Current Java enterprise application development primarily relies on a combination of manual coding and template-based generation. Developers typically complete data model design, interface definition, and business logic implementation through traditional integrated development environments (IDEs). While some low-code and visual modeling platforms can generate partial code structures through graphical operations, their core mechanism still uses static templates to replace manual coding, lacking the ability to intelligently recognize and adaptively constrain the developer's design intent. These platforms usually only focus on front-end interface layout and data binding, making it difficult to maintain semantic consistency between the modeling, code, and runtime layers. This results in discrepancies between the business semantics of the generated code and the actual design goals, which cannot be automatically corrected after the system starts running.

[0003] As business complexity and system evolution frequency increase, traditional model-driven development approaches reveal significant shortcomings in handling continuous integration and dynamic deployment scenarios. On one hand, the lack of automatic correlation between model changes and code updates necessitates manual synchronization of modifications at different levels, easily leading to logical mismatches and version conflicts. On the other hand, existing low-code platforms generally employ full replacement mechanisms for updates, failing to achieve seamless migration of database structures and service interfaces, often requiring system downtime for upgrades. Furthermore, existing methods lack mechanisms for detecting and self-correcting semantic shifts during runtime, causing models and code to gradually lose their correspondence after multiple rounds of evolution, impacting the long-term maintainability and reliability of the system.

[0004] Existing technologies cannot meet the needs of enterprise-level Java development for intelligent semantic consistency, adaptive optimization, and continuous evolution.

[0005] Therefore, how to provide a Java enterprise-level rapid development method based on visual modeling is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0006] One objective of this invention is to propose a rapid Java enterprise-level development method based on visual modeling. This invention fully utilizes a cognitive graph-driven semantic modeling mechanism, an evolutionary consistency self-correction mechanism, and multimodal zero-downtime migration technology. It details the entire process of realizing developer design intent recognition, semantic consistency detection, autonomous differential correction, and uninterrupted system evolution within a visual modeling environment. This invention achieves dynamic bidirectional mapping between design content, code implementation, and the runtime environment by constructing a semantic constraint model that integrates business design views and cognitive features. By introducing a self-correction mechanism, it automatically performs differential backtracking and design backfilling when semantic deviations are detected. Furthermore, it completes zero-downtime switching of data resources, interface resources, and policy rules through a multimodal migration relationship graph. This invention possesses advantages such as high development intelligence, strong semantic consistency between model and code, sustainable and uninterrupted system evolution, and simultaneous protection of compliance and security. It can significantly improve the development efficiency, system stability, and maintainability of enterprise-level Java applications.

[0007] A Java enterprise-level rapid development method based on visual modeling according to an embodiment of the present invention includes:

[0008] Establish a cognitive graph-driven visual modeling environment, create a business design view in the modeling interface, collect the developer's operation trajectory, node configuration order and parameter adjustment behavior, generate cognitive feature data and construct a cognitive graph;

[0009] Based on the relationship between the cognitive map and the business design view, semantic embedding and weight fusion operations are performed to fuse the developer's cognitive feature vector with the view node features to generate a unified semantic constraint model. The semantic constraint model is then input into the semantic mapping engine to generate a differential intermediate representation file.

[0010] Semantic consistency checks are performed on the differential intermediate representation file, comparing the semantic differences between the design and code. When inconsistencies are detected, a bidirectional correction process is automatically triggered to synchronously update the differences, maintaining semantic consistency between the design content and the code implementation, and generating a corrected differential intermediate representation file.

[0011] The Java project structure is automatically generated based on the corrected differential intermediate representation file. Entity classes, data access layer, service layer and interface control layer code are created according to semantic constraints. Database structure definition and interface specification documents are generated.

[0012] Construct a multimodal migration relationship graph to model the dependencies between data resources, interface resources and policy rules, establish migration dependencies and calculate migration priorities, execute zero-downtime evolution tasks, and use shadow structure and canary release strategy to achieve uninterrupted switching of database and service interface;

[0013] During runtime, the semantic consistency status of the design content, code implementation, and runtime environment is continuously monitored. When a semantic deviation is detected, a self-correction mechanism is automatically triggered to perform differential backtracking and design content backfilling operations, and the corrected design status and runtime data are synchronized and updated to the visualization modeling environment.

[0014] Optionally, creating a business design view in the modeling interface refers to defining business objects, logical processes, and data relationships in a visual modeling environment through graphical interaction, expressing the dependencies and execution order between various business modules in the form of nodes and connecting lines, uniformly describing the business structure, interface logic, and compliance rules, and setting field types, data constraints, and interaction parameters through the attribute configuration panel to form a business design view with complete semantic information.

[0015] Optionally, generating cognitive feature data and constructing a cognitive graph refers to collecting and analyzing interactive behavior data in the modeling interface in real time during the modeling process of the developer, including operational features such as node creation order, attribute modification frequency, connection path selection and naming pattern, converting operational features into quantifiable behavior vectors, and constructing the association structure of nodes and edges based on behavior similarity and logical dependency to form a cognitive graph.

[0016] Optionally, generating the differential intermediate representation file includes:

[0017] Acquire cognitive feature data and node features of business design view, and perform cleaning, tagging and unified encoding on operation type, naming pattern, sequence template, attribute configuration value, dependency relationship and constraint information to form cognitive feature set and view node feature set;

[0018] Perform semantic embedding and alignment on the cognitive feature set and the view node feature set, establish a one-to-one correspondence and a many-to-one merging relationship according to the term mapping table, dependency alignment table and naming convention table, and output the aligned semantic feature set.

[0019] Based on three evaluation indicators—name consistency, operation frequency correlation, and dependency proximity—a fusion weight configuration is generated, giving each node in the business design view a unique percentage weight and threshold, and forming a node-level fusion weight list.

[0020] A unified semantic constraint model is constructed based on the fusion weight list. This unified semantic constraint model consists of three structures: a cognitive intent lattice, a semantic anchoring graph, and a constraint propagation chain, wherein:

[0021] The cognitive intent grid organizes intent labels, triggering conditions, and sequence templates, and establishes detailed relationships.

[0022] Semantic anchoring graphs establish anchoring relationships between business design view nodes and code artifact markers, and record version identifiers and trustworthiness markers;

[0023] The constraint propagation chain converts the anchoring results into data constraints, interface constraints, and security policy constraints according to a defined execution order, and generates a rollback sequence.

[0024] The unified semantic constraint model is input into the semantic mapping engine, and differential intermediate representation files are generated according to a preset mapping order. The differential intermediate representation files include a list of structural information, a list of business logic information, and a list of security policy information.

[0025] Optionally, generating the corrected differential intermediate representation file includes:

[0026] Semantic items are extracted and standardized from the differential intermediate representation file, the design end product, and the code end product to form a comparison list covering structural information, business logic information, and security policy information. The location, category, and severity level are marked in the comparison list.

[0027] Semantic consistency checks are performed based on five dimensions: naming consistency, type matching, relational topology, execution order, and policy constraints. Inconsistency reports are generated, which include the identifier of the discrepancy, the source of the discrepancy, the scope of impact, and the level of action.

[0028] Trigger a two-way correction process when an inconsistency is detected:

[0029] Establish a peer-to-peer revision channel and generate design-side and code-side revision drafts;

[0030] Generate a change interlock ticket for each difference item. The change interlock ticket contains the triggering conditions, allowed operations, and rollback instructions.

[0031] The draft amendment is pre-validated in the shadow validation sandbox, and after passing the validation, it enters the formal application stage.

[0032] Set compliance barriers for discrepancies involving security policy information. These compliance barriers define minimum constraints that cannot be reduced and audit steps that cannot be skipped.

[0033] For discrepancies identified through the shadow verification sandbox, synchronous updates are performed according to the handling level. The design-side revision draft is used to backfill the differential intermediate representation file and business design view, while the code-side revision draft is used to update entity classes, data access layer, service layer, and interface control layer, generating version mapping tables and compatibility records.

[0034] After the update is completed, the semantic consistency check is re-executed, the comparison list is reviewed, and when it is confirmed that the differences in all dimensions are within the allowable range of the handling level, the corrected differential intermediate representation file is generated.

[0035] Optionally, the generation of database structure definition and interface specification documents includes:

[0036] Determine the corrected differential intermediate representation file and load code templates for entity classes, data access layer, service layer and interface control layer, as well as project build templates for directory and dependency organization;

[0037] Perform the mapping process from structure to entity, generate entity class and data access interface for each structure item according to the structure information list, set field type, primary key constraint, unique constraint and foreign key relationship, and write access control flag and audit flag in entity attributes and data access methods according to the security policy information list, while recording the one-to-one correspondence between entity and data table;

[0038] The process involves mapping business logic to services and controls, generating service layer classes and interface control layer classes for each logical item according to the business logic information list, setting method signatures, transaction boundaries, return codes and error codes, and writing verification and desensitization rules at the entry and exit points according to the security policy information list. At the same time, it generates interface documentation and marks endpoint identifiers, request parameters, response structures and version identifiers.

[0039] Generate database structure definitions and migration scripts, form a description of the current database schema based on the structure information list, compare the differences with the previous version of the database schema records, output a list of added, modified and deleted structure changes, generate corresponding data definition language files, migration scripts and rollback scripts, and form a structure change verification list.

[0040] Generate Java project structure and build configuration, organize source code directory, resource directory and test directory according to project build template, write dependency configuration, compilation configuration and packaging configuration, output project description file and build script, and complete the unified disk storage and archiving of compileable artifacts, interface specification documents, database scripts and version mapping records.

[0041] Optionally, the step of constructing a multimodal migration relationship graph, modeling the dependencies between data resources, interface resources, and policy rules, establishing migration dependencies, and calculating migration priorities includes:

[0042] During the runtime phase, extract the structure information list, business logic information list, and security policy information list from the differential intermediate representation file, identify data resource items, interface resource items, and policy rule items respectively, record the name, version identifier, dependency identifier, and scope of impact according to the unified identifier specification, and generate a migration candidate list.

[0043] A multimodal migration relationship graph is established around the migration candidate list. The migration dependencies between nodes are marked according to foreign key constraints, read and write paths, call chain order, transaction boundaries and strategy binding relationships. The dependency direction, triggering conditions, concurrency limits and rollback conditions are clarified to form a migration dependency table.

[0044] Based on the scope of impact, change risk level, rollback cost, online load, and observability coverage, calculate the migration priority for each item in the migration dependency table, output a deterministic migration batch number and execution order list, and specify the observation window duration and acceptance threshold for each batch.

[0045] Generate a zero-downtime evolution task plan according to the execution order list, create shadow structures for changes involving data resource items and enable dual write and change replay, create version adaptation endpoints for changes involving interface resource items and enable canary release strategy, configure read and write routes and weighted release parameters, and set audit logs, alarm thresholds and rollback contingency plans.

[0046] The migration batches are executed sequentially according to the task plan. Key indicators and audit records are verified in the observation window. When the acceptance threshold is met, the shadow structure is promoted to the main structure and the read and write routes are fixed. The weight of the gray release is increased to the target ratio and the endpoint mapping is fixed, and the corresponding batch is switched over without interruption. When the acceptance threshold is not met, the previous stable version is restored according to the rollback plan and the difference details and handling results are recorded.

[0047] Optionally, the automatic triggering of the self-correction mechanism when a semantic offset is detected, performing differential backtracking and design content backfilling operations, includes:

[0048] During runtime, data from design content, code implementation, and runtime environment are collected, and a semantic monitoring list is generated based on naming consistency, type matching, relational topology, execution order, and strategy constraints.

[0049] Set trigger conditions. When the difference exceeds the threshold or the combination of multidimensional differences meets the rules, semantic offset is determined, the self-correction process is started and a processing batch number is generated.

[0050] Construct a self-calibrating task set, setting the rewriting of the design side and differential representation file as forward correction items, and the rewriting of the code side and runtime observation as reverse correction items. Generate change interlock tickets for each item and record the source, scope of impact, priority and rollback instructions.

[0051] In the shadow verification environment, the self-correction task set is executed in the order of dependencies, backfilling the business design view on the design side, updating entity classes, data access layer, service layer and interface control layer on the code side, and synchronously updating the differential representation file and generating version mapping records.

[0052] In the observation window, the semantic monitoring list is reviewed. If the acceptance conditions are met, the corrected design status and running data are synchronized and updated to the visual modeling environment and the version record is saved. If the conditions are not met, the rollback command is used to restore the previous stable state and the self-correction task set is re-executed.

[0053] The beneficial effects of this invention are:

[0054] This invention achieves intelligent integration of developer design intent and business logic model by introducing a cognitive graph-driven modeling mechanism into a visual modeling platform. Unlike traditional static template generation methods, this invention can capture the developer's operation patterns, parameter adjustments, and logical preferences during the modeling process in real time, automatically forming a semantic constraint model to guide the code generation process. This effectively eliminates the semantic disconnect between the design and implementation layers, ensuring that the generated Java project maintains a high degree of consistency with the design content in terms of structural definition, business logic, and data interaction, thereby improving the accuracy and intelligence of code generation.

[0055] During system operation, this invention constructs a dynamic closed loop that sustainably maintains semantic consistency between the model, code, and runtime environment through evolutionary consistency detection and self-correction mechanisms. When the system detects semantic shifts or logical differences, it can automatically trigger differential backtracking and backfilling operations, achieving bidirectional updates between design content and code implementation. This ensures adaptive convergence and continuous optimization of semantics at each layer, reduces the frequency of manual intervention, avoids version drift and logical conflicts, and improves the maintainability and evolutionary stability of enterprise-level application systems.

[0056] This invention achieves seamless migration of data resources, interface resources, and policy rules by constructing a multimodal migration relationship graph. Combined with shadow structures and canary release strategies, it smoothly switches between databases and service interfaces while the system is online, eliminating the risk of downtime upgrades. This not only ensures high availability and consistency in complex update scenarios but also enhances data compliance and operational security through semantic monitoring and security policy synchronization mechanisms. In summary, this invention achieves breakthroughs in intelligent modeling, adaptive correction, and zero-downtime evolution, demonstrating significant benefits in terms of high efficiency, high reliability, and high scalability. Attached Figure Description

[0057] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0058] Figure 1 This is a flowchart of a Java enterprise-level rapid development method based on visual modeling proposed in this invention;

[0059] Figure 2This is a schematic diagram of the semantic constraint model generation and differential intermediate representation mapping process of a Java enterprise-level rapid development method based on visual modeling proposed in this invention. Detailed Implementation

[0060] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0061] refer to Figure 1 and Figure 2 A Java enterprise-level rapid development methodology based on visual modeling, comprising:

[0062] Establish a cognitive graph-driven visual modeling environment, create a business design view in the modeling interface, collect the developer's operation trajectory, node configuration order and parameter adjustment behavior, generate cognitive feature data and construct a cognitive graph;

[0063] Based on the relationship between the cognitive map and the business design view, semantic embedding and weight fusion operations are performed to fuse the developer's cognitive feature vector with the view node features to generate a unified semantic constraint model. The semantic constraint model is then input into the semantic mapping engine to generate a differential intermediate representation file.

[0064] Semantic consistency checks are performed on the differential intermediate representation file, comparing the semantic differences between the design and code. When inconsistencies are detected, a bidirectional correction process is automatically triggered to synchronously update the differences, maintaining semantic consistency between the design content and the code implementation, and generating a corrected differential intermediate representation file.

[0065] The Java project structure is automatically generated based on the corrected differential intermediate representation file. Entity classes, data access layer, service layer and interface control layer code are created according to semantic constraints. Database structure definition and interface specification documents are generated.

[0066] Construct a multimodal migration relationship graph to model the dependencies between data resources, interface resources and policy rules, establish migration dependencies and calculate migration priorities, execute zero-downtime evolution tasks, and use shadow structure and canary release strategy to achieve uninterrupted switching of database and service interface;

[0067] During runtime, the semantic consistency status of the design content, code implementation, and runtime environment is continuously monitored. When a semantic deviation is detected, a self-correction mechanism is automatically triggered to perform differential backtracking and design content backfilling operations, and the corrected design status and runtime data are synchronized and updated to the visualization modeling environment.

[0068] In this embodiment, creating a business design view in the modeling interface refers to defining business objects, logical processes, and data relationships in a visual modeling environment through graphical interaction. The dependencies and execution order between various business modules are expressed in the form of nodes and connecting lines. The business structure, interface logic, and compliance rules are uniformly described, and field types, data constraints, and interaction parameters are set through the attribute configuration panel to form a business design view with complete semantic information.

[0069] In this embodiment, generating cognitive feature data and constructing a cognitive graph refers to collecting and analyzing interactive behavior data in the modeling interface in real time during the modeling operation of the developer. This includes operational features such as node creation order, attribute modification frequency, connection path selection, and naming pattern. The operational features are then transformed into quantifiable behavior vectors, and the association structure of nodes and edges is constructed based on behavior similarity and logical dependencies to form a cognitive graph.

[0070] In this embodiment, generating the differential intermediate representation file includes:

[0071] Acquire cognitive feature data and node features of business design view, and perform cleaning, tagging and unified encoding on operation type, naming pattern, sequence template, attribute configuration value, dependency relationship and constraint information to form cognitive feature set and view node feature set;

[0072] Perform semantic embedding and alignment on the cognitive feature set and the view node feature set, establish a one-to-one correspondence and a many-to-one merging relationship according to the term mapping table, dependency alignment table and naming convention table, and output the aligned semantic feature set.

[0073] Based on three evaluation indicators—name consistency, operation frequency correlation, and dependency proximity—a fusion weight configuration is generated, giving each node in the business design view a unique percentage weight and threshold, and forming a node-level fusion weight list.

[0074] A unified semantic constraint model is constructed based on the fusion weight list. This unified semantic constraint model consists of three structures: a cognitive intent lattice, a semantic anchoring graph, and a constraint propagation chain, wherein:

[0075] The cognitive intent grid organizes intent labels, triggering conditions, and sequence templates, and establishes detailed relationships.

[0076] Semantic anchoring graphs establish anchoring relationships between business design view nodes and code artifact markers, and record version identifiers and trustworthiness markers;

[0077] The constraint propagation chain converts the anchoring results into data constraints, interface constraints, and security policy constraints according to a defined execution order, and generates a rollback sequence.

[0078] The unified semantic constraint model is input into the semantic mapping engine, and differential intermediate representation files are generated according to a preset mapping order. The differential intermediate representation files include a list of structural information, a list of business logic information, and a list of security policy information.

[0079] In this embodiment, generating the corrected differential intermediate representation file includes:

[0080] Semantic items are extracted and standardized from the differential intermediate representation file, the design end product, and the code end product to form a comparison list covering structural information, business logic information, and security policy information. The location, category, and severity level are marked in the comparison list.

[0081] Semantic consistency checks are performed based on five dimensions: naming consistency, type matching, relational topology, execution order, and policy constraints. Inconsistency reports are generated, which include the identifier of the discrepancy, the source of the discrepancy, the scope of impact, and the level of action.

[0082] Trigger a two-way correction process when an inconsistency is detected:

[0083] Establish a peer-to-peer revision channel and generate design-side and code-side revision drafts;

[0084] Generate a change interlock ticket for each difference item. The change interlock ticket contains the triggering conditions, allowed operations, and rollback instructions.

[0085] The draft amendment is pre-validated in the shadow validation sandbox, and after passing the validation, it enters the formal application stage.

[0086] Set compliance barriers for discrepancies involving security policy information. These compliance barriers define minimum constraints that cannot be reduced and audit steps that cannot be skipped.

[0087] For discrepancies identified through the shadow verification sandbox, synchronous updates are performed according to the handling level. The design-side revision draft is used to backfill the differential intermediate representation file and business design view, while the code-side revision draft is used to update entity classes, data access layer, service layer, and interface control layer, generating version mapping tables and compatibility records.

[0088] After the update is completed, the semantic consistency check is re-executed, the comparison list is reviewed, and when it is confirmed that the differences in all dimensions are within the allowable range of the handling level, the corrected differential intermediate representation file is generated.

[0089] In this embodiment, the generation of database structure definition and interface specification documents includes:

[0090] Determine the corrected differential intermediate representation file and load code templates for entity classes, data access layer, service layer and interface control layer, as well as project build templates for directory and dependency organization;

[0091] Perform the mapping process from structure to entity, generate entity class and data access interface for each structure item according to the structure information list, set field type, primary key constraint, unique constraint and foreign key relationship, and write access control flag and audit flag in entity attributes and data access methods according to the security policy information list, while recording the one-to-one correspondence between entity and data table;

[0092] The process involves mapping business logic to services and controls, generating service layer classes and interface control layer classes for each logical item according to the business logic information list, setting method signatures, transaction boundaries, return codes and error codes, and writing verification and desensitization rules at the entry and exit points according to the security policy information list. At the same time, it generates interface documentation and marks endpoint identifiers, request parameters, response structures and version identifiers.

[0093] Generate database structure definitions and migration scripts, form a description of the current database schema based on the structure information list, compare the differences with the previous version of the database schema records, output a list of added, modified and deleted structure changes, generate corresponding data definition language files, migration scripts and rollback scripts, and form a structure change verification list.

[0094] Generate Java project structure and build configuration, organize source code directory, resource directory and test directory according to project build template, write dependency configuration, compilation configuration and packaging configuration, output project description file and build script, and complete the unified disk storage and archiving of compileable artifacts, interface specification documents, database scripts and version mapping records.

[0095] In this embodiment, the construction of a multimodal migration relationship graph, which models the dependencies between data resources, interface resources, and policy rules, establishes migration dependencies, and calculates migration priorities, includes:

[0096] During the runtime phase, extract the structure information list, business logic information list, and security policy information list from the differential intermediate representation file, identify data resource items, interface resource items, and policy rule items respectively, record the name, version identifier, dependency identifier, and scope of impact according to the unified identifier specification, and generate a migration candidate list.

[0097] A multimodal migration relationship graph is established around the migration candidate list. The migration dependencies between nodes are marked according to foreign key constraints, read and write paths, call chain order, transaction boundaries and strategy binding relationships. The dependency direction, triggering conditions, concurrency limits and rollback conditions are clarified to form a migration dependency table.

[0098] Based on the scope of impact, change risk level, rollback cost, online load, and observability coverage, calculate the migration priority for each item in the migration dependency table, output a deterministic migration batch number and execution order list, and specify the observation window duration and acceptance threshold for each batch.

[0099] Generate a zero-downtime evolution task plan according to the execution order list, create shadow structures for changes involving data resource items and enable dual write and change replay, create version adaptation endpoints for changes involving interface resource items and enable canary release strategy, configure read and write routes and weighted release parameters, and set audit logs, alarm thresholds and rollback contingency plans.

[0100] The migration batches are executed sequentially according to the task plan. Key indicators and audit records are verified in the observation window. When the acceptance threshold is met, the shadow structure is promoted to the main structure and the read and write routes are fixed. The weight of the gray release is increased to the target ratio and the endpoint mapping is fixed, and the corresponding batch is switched over without interruption. When the acceptance threshold is not met, the previous stable version is restored according to the rollback plan and the difference details and handling results are recorded.

[0101] In this embodiment, the automatic triggering of the self-correction mechanism when a semantic offset is detected, and the execution of differential backtracking and design content backfilling operations, include:

[0102] During runtime, data from design content, code implementation, and runtime environment are collected, and a semantic monitoring list is generated based on naming consistency, type matching, relational topology, execution order, and strategy constraints.

[0103] Set trigger conditions. When the difference exceeds the threshold or the combination of multidimensional differences meets the rules, semantic offset is determined, the self-correction process is started and a processing batch number is generated.

[0104] Construct a self-calibrating task set, setting the rewriting of the design side and differential representation file as forward correction items, and the rewriting of the code side and runtime observation as reverse correction items. Generate change interlock tickets for each item and record the source, scope of impact, priority and rollback instructions.

[0105] In the shadow verification environment, the self-correction task set is executed in the order of dependencies, backfilling the business design view on the design side, updating entity classes, data access layer, service layer and interface control layer on the code side, and synchronously updating the differential representation file and generating version mapping records.

[0106] In the observation window, the semantic monitoring list is reviewed. If the acceptance conditions are met, the corrected design status and running data are synchronized and updated to the visual modeling environment and the version record is saved. If the conditions are not met, the rollback command is used to restore the previous stable state and the self-correction task set is re-executed.

[0107] Example 1:

[0108] To verify the feasibility of this invention in practice, it was applied to the information system of a large manufacturing enterprise. The enterprise's original system was developed using the traditional Java Spring Boot framework, encompassing five core modules: equipment management, production scheduling, energy consumption monitoring, quality inspection, and report analysis. Due to the system's long runtime and frequent adjustments to business logic, the traditional development approach encountered significant bottlenecks during code updates and database migrations: models and code were often out of sync, version evolution required system downtime for updates, and interface compatibility was poor, severely impacting the continuity of production operations and data reliability.

[0109] In the project transformation, the system was rebuilt using the method of this invention. A business design view was created in a visual modeling environment, visually displaying the relationship between production tasks, equipment status, and energy consumption data in a node-based manner. By collecting the developer's operation sequence, attribute configuration, and dependencies in the modeling interface, the system automatically generates cognitive feature data and constructs a cognitive graph to characterize the developer's logical intent. Subsequently, based on the association between the cognitive graph and the business design view, the system performs semantic embedding and fusion to generate a unified semantic constraint model. This model is transformed into a differential intermediate representation file in the semantic mapping engine, containing structural information, business logic information, and security policy information, providing a standardized data foundation for subsequent code generation and continuous evolution.

[0110] During the code generation phase, the system automatically constructs the Java project structure based on the semantic constraint model, generating code for entity classes, data access layer, service layer, and interface control layer. Taking the production scheduling module as an example, the system automatically generated 176 class files within 4 hours, with database scripts output synchronously, requiring no manual coding intervention. Compared to previous manual implementations, development time is reduced by approximately 68%. In subsequent operation, the system monitors the differences between the design content and the code implementation through a semantic consistency detection mechanism. Once issues such as logical deviations, inconsistent interface naming, or security policy mismatches are detected, a self-correction mechanism is automatically triggered, performing differential backtracking and design backfilling operations in a shadow environment to ensure real-time consistency between the model, code, and runtime state.

[0111] During the deployment phase, the multimodal migration graph constructed using the method of this invention was used for database and interface version switching. By modeling the dependencies between data resources, interface resources, and policy rules, the system calculates migration priorities and executes zero-downtime evolution tasks. Within the implementation period, the company completed two system upgrades without downtime: one for restructuring the equipment monitoring module and the other for updating the energy consumption assessment algorithm. The system achieved smooth migration through a shadow structure and a canary release mechanism, without any service interruptions or data anomalies.

[0112] Table 1. Comparison of the application effects of the method of the present invention in the information system of manufacturing enterprises.

[0113]

[0114] As can be seen from the data in Table 1, this invention improves the overall efficiency and system performance of enterprise-level Java development in practical applications. In terms of development efficiency, the average module development cycle is shortened from 22.5 days in the traditional method to 13.8 days, a reduction of approximately 38.7%, and the average code generation time is shortened from 12.6 hours to 3.9 hours, an improvement of 69%. The visual modeling and automated generation mechanism of this invention effectively reduces manual coding workload, enables rapid mapping from business logic to code implementation, and significantly improves the system's development progress and delivery speed.

[0115] Regarding semantic consistency, this invention significantly improves the matching accuracy between the model and code through a cognitive graph-driven semantic constraint model. Test results show that the pass rate for semantic consistency detection between the model and code increases from 84.2% in the traditional method to 98.3%, effectively avoiding model deviation and logical inconsistencies. During the system evolution phase, this invention utilizes a multimodal migration relationship graph to achieve zero-downtime switching between the database and interfaces, reducing the average downtime for system upgrades from 35 minutes to 0 minutes, and increasing the zero-downtime migration success rate from 93.5% to 99.6%, ensuring the continuous availability and business continuity of the system during version evolution.

[0116] In terms of intelligent self-correction and resource utilization, this invention automatically repairs semantic offsets through semantic monitoring and self-correction mechanisms, achieving a 98.5% success rate with an average response time of only 3.4 minutes, significantly outperforming traditional methods that rely on manual intervention. Developer workload is reduced from 410 person-days / month to 278 person-days / month, saving approximately 32% in labor costs. Regarding operational stability, the core module anomaly recovery time is shortened from 28 minutes to 9 minutes, and system service availability is increased from 99.3% to 99.97%. This invention demonstrates higher reliability and continuous operation capability in actual production environments. This invention exhibits significant advantages in multiple dimensions, including development speed, system consistency, self-correction capability, and operational stability, possessing outstanding engineering practical value and promotional potential.

[0117] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A Java enterprise-level rapid development method based on visual modeling, characterized in that, include: Establish a cognitive graph-driven visual modeling environment, create a business design view in the modeling interface, collect the developer's operation trajectory, node configuration order and parameter adjustment behavior, generate cognitive feature data and construct a cognitive graph; Based on the relationship between the cognitive map and the business design view, semantic embedding and weight fusion operations are performed to fuse the developer's cognitive feature vector with the view node features to generate a unified semantic constraint model. The semantic constraint model is then input into the semantic mapping engine to generate a differential intermediate representation file. Semantic consistency checks are performed on the differential intermediate representation file, comparing the semantic differences between the design and code. When inconsistencies are detected, a bidirectional correction process is automatically triggered to synchronously update the differences, maintaining semantic consistency between the design content and the code implementation, and generating a corrected differential intermediate representation file. The Java project structure is automatically generated based on the corrected differential intermediate representation file. Entity classes, data access layer, service layer and interface control layer code are created according to semantic constraints. Database structure definition and interface specification documents are generated. Construct a multimodal migration relationship graph to model the dependencies between data resources, interface resources and policy rules, establish migration dependencies and calculate migration priorities, execute zero-downtime evolution tasks, and use shadow structure and canary release strategy to achieve uninterrupted switching of database and service interface; During runtime, the semantic consistency status of the design content, code implementation, and runtime environment is continuously monitored. When a semantic deviation is detected, a self-correction mechanism is automatically triggered to perform differential backtracking and design content backfilling operations, and the corrected design status and runtime data are synchronized and updated to the visualization modeling environment.

2. The Java enterprise-level rapid development method based on visual modeling according to claim 1, characterized in that, Creating a business design view in the modeling interface refers to defining business objects, logical processes, and data relationships through graphical interaction in a visual modeling environment. It uses nodes and connecting lines to express the dependencies and execution order between various business modules, provides a unified description of the business structure, interface logic, and compliance rules, and sets field types, data constraints, and interaction parameters through the attribute configuration panel to form a business design view with complete semantic information.

3. The Java enterprise-level rapid development method based on visual modeling according to claim 1, characterized in that, The generation of cognitive feature data and the construction of cognitive graphs refer to the real-time collection and analysis of interactive behavior data in the modeling interface during the modeling process of developers. This includes operational features such as node creation order, attribute modification frequency, connection path selection, and naming patterns. The operational features are then transformed into quantifiable behavioral vectors, and the association structure of nodes and edges is constructed based on behavioral similarity and logical dependencies to form a cognitive graph.

4. The Java enterprise-level rapid development method based on visual modeling according to claim 1, characterized in that, The generation of the differential intermediate representation file includes: Acquire cognitive feature data and node features of business design view, and perform cleaning, tagging and unified encoding on operation type, naming pattern, sequence template, attribute configuration value, dependency relationship and constraint information to form cognitive feature set and view node feature set; Perform semantic embedding and alignment on the cognitive feature set and the view node feature set, establish a one-to-one correspondence and a many-to-one merging relationship according to the term mapping table, dependency alignment table and naming convention table, and output the aligned semantic feature set. Based on three evaluation indicators—name consistency, operation frequency correlation, and dependency proximity—a fusion weight configuration is generated, giving each node in the business design view a unique percentage weight and threshold, and forming a node-level fusion weight list. A unified semantic constraint model is constructed based on the fusion weight list. This unified semantic constraint model consists of three structures: a cognitive intent lattice, a semantic anchoring graph, and a constraint propagation chain, wherein: The cognitive intent grid organizes intent labels, triggering conditions, and sequence templates, and establishes detailed relationships. Semantic anchoring graphs establish anchoring relationships between business design view nodes and code artifact markers, and record version identifiers and trustworthiness markers; The constraint propagation chain converts the anchoring results into data constraints, interface constraints, and security policy constraints according to a defined execution order, and generates a rollback sequence. The unified semantic constraint model is input into the semantic mapping engine, and differential intermediate representation files are generated according to a preset mapping order. The differential intermediate representation files include a list of structural information, a list of business logic information, and a list of security policy information.

5. The Java enterprise-level rapid development method based on visual modeling according to claim 1, characterized in that, The generation of the corrected differential intermediate representation file includes: Semantic items are extracted and standardized from the differential intermediate representation file, the design end product, and the code end product to form a comparison list covering structural information, business logic information, and security policy information. The location, category, and severity level are marked in the comparison list. Semantic consistency checks are performed based on five dimensions: naming consistency, type matching, relational topology, execution order, and policy constraints. Inconsistency reports are generated, which include the identifier of the discrepancy, the source of the discrepancy, the scope of impact, and the handling level. Trigger a two-way correction process when an inconsistency is detected: Establish a peer-to-peer revision channel and generate design-side and code-side revision drafts; Generate a change interlock ticket for each difference item. The change interlock ticket contains the triggering conditions, allowed operations, and rollback instructions. The draft amendment is pre-validated in the shadow validation sandbox, and after passing the validation, it enters the formal application stage. Set compliance barriers for discrepancies involving security policy information. These compliance barriers define minimum constraints that cannot be reduced and audit steps that cannot be skipped. For discrepancies identified through the shadow verification sandbox, synchronous updates are performed according to the handling level. The design-side revision draft is used to backfill the differential intermediate representation file and business design view, while the code-side revision draft is used to update entity classes, data access layer, service layer, and interface control layer, generating version mapping tables and compatibility records. After the update is completed, the semantic consistency check is re-executed, the comparison list is reviewed, and when it is confirmed that the differences in all dimensions are within the allowable range of the handling level, the corrected differential intermediate representation file is generated.

6. The Java enterprise-level rapid development method based on visual modeling according to claim 1, characterized in that, The generated database structure definition and interface specification document includes: Determine the corrected differential intermediate representation file and load code templates for entity classes, data access layer, service layer and interface control layer, as well as project build templates for directory and dependency organization; Perform the mapping process from structure to entity, generate entity class and data access interface for each structure item according to the structure information list, set field type, primary key constraint, unique constraint and foreign key relationship, and write access control flag and audit flag in entity attributes and data access methods according to the security policy information list, while recording the one-to-one correspondence between entity and data table; The process involves mapping business logic to services and controls, generating service layer classes and interface control layer classes for each logical item according to the business logic information list, setting method signatures, transaction boundaries, return codes and error codes, and writing verification and desensitization rules at the entry and exit points according to the security policy information list. At the same time, it generates interface documentation and marks endpoint identifiers, request parameters, response structures and version identifiers. Generate database structure definitions and migration scripts, form a description of the current database schema based on the structure information list, compare the differences with the previous version of the database schema records, output a list of added, modified and deleted structure changes, generate corresponding data definition language files, migration scripts and rollback scripts, and form a structure change verification list. Generate Java project structure and build configuration, organize source code directory, resource directory and test directory according to project build template, write dependency configuration, compilation configuration and packaging configuration, output project description file and build script, and complete the unified disk storage and archiving of compileable artifacts, interface specification documents, database scripts and version mapping records.

7. The Java enterprise-level rapid development method based on visual modeling according to claim 1, characterized in that, The construction of a multimodal migration relationship graph models the dependencies between data resources, interface resources, and policy rules, establishes migration dependencies, and calculates migration priorities, including: During the runtime phase, extract the structure information list, business logic information list, and security policy information list from the differential intermediate representation file, identify data resource items, interface resource items, and policy rule items respectively, record the name, version identifier, dependency identifier, and scope of impact according to the unified identifier specification, and generate a migration candidate list. A multimodal migration relationship graph is established around the migration candidate list. The migration dependencies between nodes are marked according to foreign key constraints, read and write paths, call chain order, transaction boundaries and strategy binding relationships. The dependency direction, triggering conditions, concurrency limits and rollback conditions are clarified to form a migration dependency table. Based on the scope of impact, change risk level, rollback cost, online load, and observability coverage, calculate the migration priority for each item in the migration dependency table, output a deterministic migration batch number and execution order list, and specify the observation window duration and acceptance threshold for each batch. Generate a zero-downtime evolution task plan according to the execution order list, create shadow structures for changes involving data resource items and enable dual write and change replay, create version adaptation endpoints for changes involving interface resource items and enable canary release strategy, configure read and write routes and weighted release parameters, and set audit logs, alarm thresholds and rollback contingency plans. The migration batches are executed sequentially according to the task plan. Key indicators and audit records are verified in the observation window. When the acceptance threshold is met, the shadow structure is promoted to the main structure and the read and write routes are fixed. The weight of the gray release is increased to the target ratio and the endpoint mapping is fixed, and the corresponding batch is switched over without interruption. When the acceptance threshold is not met, the previous stable version is restored according to the rollback plan and the difference details and handling results are recorded.

8. The Java enterprise-level rapid development method based on visual modeling according to claim 1, characterized in that, The automatic self-correction mechanism triggered when a semantic offset is detected performs differential backtracking and design content backfilling operations, including: During runtime, data from design content, code implementation, and runtime environment are collected, and a semantic monitoring list is generated based on naming consistency, type matching, relational topology, execution order, and strategy constraints. Set trigger conditions. When the difference exceeds the threshold or the combination of multidimensional differences meets the rules, semantic offset is determined, the self-correction process is started and a processing batch number is generated. Construct a self-calibrating task set, setting the rewriting of the design side and differential representation file as forward correction items, and the rewriting of the code side and runtime observation as reverse correction items. Generate change interlock tickets for each item and record the source, scope of impact, priority and rollback instructions. In the shadow verification environment, the self-correction task set is executed in the order of dependencies, backfilling the business design view on the design side, updating entity classes, data access layer, service layer and interface control layer on the code side, and synchronously updating the differential representation file and generating version mapping records. In the observation window, the semantic monitoring list is reviewed. If the acceptance conditions are met, the corrected design status and running data are synchronized and updated to the visual modeling environment and the version record is saved. If the conditions are not met, the rollback command is used to restore the previous stable state and the self-correction task set is re-executed.

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