Java-based industry operation permission intelligent approval system and implementation method

Abstract

The invention discloses an industry operation permission intelligent approval system based on Java and an implementation method, and belongs to the technical field of computer application. The system adopts a four-layer architecture including a presentation layer, a business logic layer, a data access layer and a data storage layer. The core of the system is that the business logic layer integrates an intelligent auditing engine, a rule engine and a workflow engine; the intelligent auditing engine automatically analyzes and extracts key information in the application material through NLP and OCR technologies; a rule system based on industry classification is built in the rule engine, three-dimensional automatic verification of subject qualification, material integrity and condition conformity is executed, and regulatory terms can be mapped into executable logic; and the workflow engine automatically triggers and manages the approval process according to the verification result. According to the invention, full-process automation and intellectualization of industry operation permission approval are realized, the approval efficiency, accuracy and dynamic adaptation capability to policies and regulations are greatly improved, and the problems of low efficiency, different standards, lagging and the like existing in a traditional manual approval mode are effectively solved.

Description

Technical Field

[0001] This invention relates to the field of computer application technology, and in particular to an intelligent approval system for industry business licenses based on Java and its implementation method. Background Technology

[0002] In the current industry business license approval process, the traditional approval model has many drawbacks that urgently need to be addressed. Applicants are required to submit a large amount of paper materials, which often lead to duplicate reviews and information delays as they circulate among multiple departments. This not only causes applicants to waste a significant amount of time and energy, delaying business operations, but also forces approval departments to invest considerable manpower in repetitive tasks such as material verification and data entry, resulting in a waste of time and human resources. Furthermore, manual processing can easily lead to problems such as low approval efficiency, inconsistent application of approval standards, and discrepancies in results due to differences in the professional level of reviewers, subjective judgment biases, and oversights in information verification. At the same time, with the rapid innovation of industry formats and the dynamic adjustment of laws and policies, the traditional approval model, which relies on manual updates of approval rules and processes, suffers from significant lag and difficulty in quickly responding to policy changes. This is especially true in the licensing of emerging industries, where unclear approval criteria and insufficient process adaptability often arise, greatly hindering the compliance and efficiency of the approval process. Based on the above situation, there is an urgent need for an industry business license approval system and implementation method with efficient processing capabilities, intelligent review mechanisms, and flexible adaptability, so as to completely solve the inherent defects of the traditional approval model. Summary of the Invention 1. The inefficiency caused by the cumbersome approval process is manifested in the repeated review of paper materials in the circulation of multiple departments and the lag in information transmission, resulting in the waste of time costs for applicants and the redundant manpower investment of approval departments. 2. Problems of insufficient standardization in the approval process caused by excessive human intervention, including inconsistent implementation of approval standards due to differences in the professional level of reviewers and subjective judgment bias, as well as result deviations and potential administrative disputes caused by information verification omissions. 3. Insufficient adaptability to dynamic changes in laws and policies, namely, the reliance on manual updates of approval rules and procedures is significantly lagging behind, especially in the approval of emerging industries where there are unclear bases and poor process adaptability, making it difficult to respond quickly to policy adjustment needs. Attached Figure Description

[0003] Figure 1 This is a diagram of the overall architecture of the present invention. Figure 2 This is the four-layer interaction diagram of the present invention. Figure 3 This is a flowchart of the intelligent approval process of the present invention. Figure 4 This is a flowchart of the intelligent review engine of the present invention. Figure 5 This is a flowchart of the rule engine workflow of the present invention. Detailed Implementation

[0004] 1. System Setup: Based on the Java development platform, the system's basic framework is built using the Spring Cloud microservice architecture, achieving decoupling and efficient interaction between the presentation layer, business logic layer, data access layer, and data storage layer. The presentation layer uses Vue.js to develop a responsive web interface and React Native to build a cross-platform mobile application, supporting adaptive form rendering and multi-terminal data synchronization. The data access layer is adapted to the DM database, uses MyBatis to achieve interaction with a domestic database, and integrates Redis to cache frequently accessed data. Spring Security is used to implement RBAC-based access control, ensuring clear boundaries for different roles and meeting the requirements of domestic government system development and security.

[0005] 2. Intelligent Review Engine Implementation: A large amount of historical approval cases and industry regulations are collected as training data. Natural language processing technology is used to preprocess the data, including word segmentation, part-of-speech tagging, and entity recognition. A deep learning model (such as BERT) is used for training, enabling the intelligent review engine to accurately extract key information from application materials, such as company name, business scope, and registered capital, and automatically compare it with preset approval conditions.

[0006] 3. Rule Engine Configuration: The rule engine is built on the Drools framework, employing a three-layer mapping structure of "clause-rule-parameter": the bottom layer connects to the government regulations database; the middle layer uses a visual configuration platform (supporting drag-and-drop logic arrangement) to convert regulatory clauses into executable rules (such as "automatically trigger the correction process if application materials are missing"); and the top layer sets dynamic parameters (such as qualification validity thresholds and material correction deadlines). The rule engine supports version management and conflict detection. When a new regulation is published, the system automatically marks the rule as needing to be updated and pushes revision suggestions. After the approver completes the rule adjustment through the interface, it takes effect immediately without restarting the system.

[0007] 4. Workflow Engine Application: A visual process modeling platform is built based on the Activiti engine, supporting the graphical definition of approval processes (including serial / parallel nodes, branch conditions, and countersigning rules). By configuring process template parameters (such as node processing time limits, jump conditions, and approval permissions), automatic task allocation is achieved: the system matches the corresponding process template based on the application type, pushes tasks to idle approvers through a load balancing algorithm, and integrates message middleware (such as RabbitMQ) to trigger multi-channel reminders (SMS, in-system messages, and government collaboration platform notifications). All approval node operations are fully traceable, supporting process instance pause, resumption, and abnormal jumps to meet the flexibility requirements of complex approval scenarios.

[0008] 5. System Testing and Optimization: After system development is complete, comprehensive testing will be conducted. Different approval scenarios will be simulated, such as normal applications, incomplete materials, and failure to meet approval conditions, to test the system's functional integrity and stability. Extensive test data will be used to verify the system's performance, such as response time and concurrent processing capabilities. Based on the test results, the system will be optimized, such as adjusting algorithm parameters and optimizing database query statements, to improve the system's operating efficiency and accuracy. Simultaneously, data encryption technology will be used to encrypt and store sensitive data, and strict identity authentication and access control mechanisms will be established to ensure the security of system data.

[0009] Through the above specific implementation methods, the Java-based intelligent approval system for industry business licenses of the present invention can realize the intelligentization, automation and standardization of industry business license approval, effectively improve approval efficiency and quality, and provide strong technical support for industry business license approval work.

Claims

1. A Java-based intelligent approval system for industry business licenses, characterized in that, include: The presentation layer provides web and mobile access points for entering application information and uploading electronic materials; The business logic layer includes an intelligent review engine, a rules engine, and a workflow engine. The intelligent review engine parses and extracts material information using NLP and OCR. The rules engine includes an industry classification rule system, a three-dimensional verification module, a legal clause mapping module, and a rule dynamic update module. The workflow engine triggers the approval process based on the rule results and records four-element logs. The data access layer uses MyBatis and Redis to implement data operations and caching. The data storage layer uses DM database and HDFS to store structured and unstructured data, respectively.

2. The system according to claim 1, characterized in that: The intelligent audit engine uses the BERT model for entity recognition, extracting company names, business scope, and registered capital, and comparing OCR text with structured data.

3. The system according to claim 1, characterized in that: The rules engine is built on the Drools framework and provides a visual configuration interface. The legal clause mapping module converts regulations into executable rules and sets parameters such as qualification validity period and correction deadline.

4. The system according to claim 1, characterized in that: The workflow engine is based on the Activiti framework and integrates the RabbitMQ message middleware. It distributes tasks through load balancing and supports pausing and resuming process instances.

5. The system according to claim 1, characterized in that: The data storage layer deploys consortium blockchain nodes, and the blockchain evidence storage includes material hashes, approval conclusions, timestamps, and digital signatures.

6. An intelligent approval method based on any one of claims 1–5, comprising the following steps: Receive application data through the user interface; use NLP and OCR to parse materials and extract enterprise credit code, business address and qualification certificate number; The rules engine is invoked to perform three-dimensional verification: subject qualification verification, material completeness verification, and condition compliance verification; the approval process is automatically triggered based on the verification score: high score follows the fast process, medium score follows the standard process, and low score triggers rejection; The regulatory database is synchronized daily, and conflicts are detected through rule dependency graphs. The approval results are output after being stored on the blockchain.

7. The method according to claim 6, characterized in that, Dynamic rule updates include: generating optimization suggestions when the rule rejection rate exceeds a threshold; managing rule base versions based on semantic version numbers; and recording the subject, basis, and effective date of rule revisions.

8. The method according to claim 6, characterized in that, When approving across departments, the verification results are shared in real time, the relevant department's rule subset is automatically called, and a reverse traceability chain between the rule ID and the approval form is established.

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