Multi-scene vertical service system generation platform

Abstract

The invention provides a multi-scene vertical service system generation platform, and belongs to the technical field of software engineering and automatic development. Comprising the following steps that 1, a multi-dimensional scene meta-model library is constructed, and the meta-model library comprises scene feature metadata, business process metadata, data structure metadata and interface adaptation metadata in the vertical business field; 2, scene demand parameters input by a user are received; step 3, a demand analysis mechanism based on combination of natural language processing and a rule engine; and step 5, performing quantitative scoring on the candidate service system framework through a multi-dimensional evaluation model, evaluating dimensions including function matching degree, expansibility, compatibility, deployment convenience and operation and maintenance cost, and screening out an optimal framework. Through a standardized meta-model library and an intelligent algorithm, a service system which is highly adaptive to a specific vertical service scene and has excellent expansibility and stability is automatically and efficiently generated, and the development cost and the technical threshold are remarkably reduced.

Description

Technical Field

[0001] This invention provides a multi-scenario vertical business system generation platform, belonging to the field of software engineering and automated development technology. Background Technology

[0002] With the deepening of digital transformation, the demand for customized business systems in various vertical industries (such as finance, healthcare, logistics, and retail) is growing. Traditional software development models suffer from long development cycles, high labor costs, high technical barriers, and difficulty in quickly responding to business changes. While low-code / no-code platforms have improved development efficiency to some extent, their general applicability often makes it difficult to deeply match specific business processes, data structures, and performance requirements in complex vertical business scenarios, resulting in insufficient professionalism, scalability, and stability of the generated systems.

[0003] Based on the above, the inventors discovered that:

[0004] Existing system generation methods are mostly based on fixed templates or limited component libraries, lacking a deep understanding of vertical business domain knowledge and the ability to flexibly combine them. They are usually unable to effectively handle multi-dimensional and mutually constraining user needs (such as balancing high concurrency performance, complex business logic, and specific deployment environments), and have significant shortcomings in system architecture optimization, automated testing, and correction.

[0005] Therefore, in view of this, we study and improve the existing structure, and propose a multi-scenario vertical business system generation platform to solve the above-mentioned problems. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a multi-scenario vertical business system generation platform. This platform can automatically and efficiently generate business systems that are highly adapted to specific vertical business scenarios and have excellent scalability and stability through a standardized meta-model library and intelligent algorithms, significantly reducing development costs and technical barriers.

[0007] To address the aforementioned problems, the present invention proposes the following technical solution: a multi-scenario vertical business system generation platform, comprising the following steps:

[0008] Step 1: Construct a multi-dimensional scenario meta-model library. The meta-model library contains scenario feature metadata, business process metadata, data structure metadata, and interface adaptation metadata for vertical business domains. The metadata is classified and indexed through a standardized tag system, which includes four core dimensions: scenario type, business complexity, data volume, and interface protocol type.

[0009] Step 2: Receive the scenario requirement parameters input by the user. The requirement parameters include business domain type, scenario functional requirements, performance indicator thresholds, external system integration requirements, and deployment environment constraints.

[0010] Step 3: Based on the requirement parsing mechanism combining natural language processing and rule engine, map the scenario requirement parameters to the target metadata set in the metamodel library mentioned in Step 1 to generate a requirement-metadata mapping relationship table; Step 4: Adopt a metadata combination optimization strategy based on genetic algorithm, select and combine suitable metadata according to the mapping relationship table to generate at least 3 sets of candidate business system frameworks. The fitness function of the genetic algorithm has the optimization objectives of business matching degree, development efficiency improvement rate, and system resource utilization rate;

[0011] Step 5: Quantitatively score the candidate business system frameworks using a multi-dimensional evaluation model. Evaluation dimensions include functional matching, scalability, compatibility, ease of deployment, and operation and maintenance costs, and select the optimal framework.

[0012] Step 6: Automatically generate core business code, configuration files, and deployment scripts based on the optimal framework. The core business code adopts a plug-in architecture design, which supports hot-swapping of functional modules.

[0013] Step 7: Build a dynamic simulation test environment to perform scenario-based stress testing, compatibility testing, and exception handling testing on the generated business system, and generate a test report;

[0014] Step 8: Automatically correct metadata combination deviations and code logic defects based on the test report. If the corrected system meets the preset indicator thresholds, output the complete business system package; otherwise, return to step 4 to re-optimize the metadata combination.

[0015] Furthermore, the standardized tagging system described in step 1 adopts a three-level tagging structure: the first-level tags are business domain classifications, the second-level tags are scenario feature subdivisions, and the third-level tags are technology adaptation attributes. The meta-model library also supports user-defined tag expansion and dynamic updates of metadata.

[0016] Furthermore, the requirement parsing mechanism described in step 3 includes: firstly, using the BERT model to perform semantic understanding and keyword extraction on the natural language requirements input by the user; secondly, using a preset business rule base to perform structured transformation on the extracted keywords; and finally, establishing an accurate mapping with the metadata.

[0017] Furthermore, the optimization process of the genetic algorithm in step 4 includes: initializing the population as a random metadata combination scheme, retaining the top 60% of individuals by selecting operators, recombining the metadata modules of different individuals by crossover operators, randomly replacing some metadata modules by mutation operators, and iterating until the fitness value converges or reaches the preset number of iterations.

[0018] Furthermore, the multi-dimensional evaluation model described in step 5 uses the analytic hierarchy process (AHP) to determine the weights of each evaluation dimension, wherein the weight of functional matching degree is not less than 30%, the weight of scalability is not less than 20%, the weight of compatibility is not less than 15%, the weight of deployment convenience is not less than 15%, and the weight of operation and maintenance cost is not less than 20%.

[0019] Furthermore, the plug-in architecture design described in step 6 includes a core framework layer, a plug-in interface layer, and a functional plug-in layer. The core framework layer provides a basic operating environment, the plug-in interface layer defines standardized interaction interfaces, and the functional plug-in layer includes business logic plug-ins, data processing plug-ins, and interface adaptation plug-ins. Each plug-in communicates with the core framework layer through the interface layer.

[0020] Furthermore, the dynamic simulation test environment described in step 7 supports automatic configuration of the test dataset size, number of concurrent users, and external dependent simulation services according to the business scenario. The number of concurrent users in the stress test can be dynamically adjusted within the range of 10-10000, and the test dataset size can be expanded by 1-10 times the business data volume.

[0021] Furthermore, step 9 is included: establishing a full lifecycle management mechanism for the business system, including operational status monitoring, functional module upgrades, vulnerability patching, and system expansion, supporting automatic adjustment of system resource configuration based on business growth trends.

[0022] Furthermore, the meta-model library mentioned in step 1 aggregates vertical business data from multiple industries through a federated learning mechanism, achieving cross-domain optimization of metadata while ensuring data privacy. Moreover, the metadata updates adopt an incremental update strategy, synchronizing only newly added and changed metadata information.

[0023] Furthermore, the automatically generated core business code in step 6 supports output in multiple programming languages, including Java, Python, Go, and .NET, and the configuration file supports multiple formats such as YAML, JSON, and XML. The deployment script is compatible with Docker, Kubernetes, and physical machine deployment environments.

[0024] Due to the adoption of the above technical solution, the beneficial effects of the multi-scenario vertical business system generation platform of the present invention are as follows:

[0025] 1. High level of intelligence and automation: By combining natural language processing, genetic algorithms and multi-dimensional evaluation models, the entire process from requirement understanding and architecture design to code generation and testing is automated, which greatly reduces human intervention. Genetic algorithms are used for multi-objective optimization, and after generation, the system undergoes rigorous dynamic simulation testing to ensure that the output system achieves a high level in terms of functionality, performance, scalability and stability.

[0026] 2. Deep vertical domain adaptation: Based on a carefully constructed multi-dimensional meta-model library, it can deeply understand and match the specific business logic, data characteristics and interface specifications of different vertical industries, resulting in highly professional systems.

[0027] 3. Flexibility and scalability: The plug-in architecture design and the meta-model library that supports custom extensions enable the generated system and platform to easily adapt to future business and technology changes. It integrates post-generation monitoring, upgrade, and expansion capabilities, providing users with an end-to-end solution. Attached Figure Description

[0028] 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:

[0029] Figure 1 This is a flowchart illustrating the overall generation process of a multi-scenario vertical business system generation platform according to the present invention.

[0030] Figure 2 This is a flowchart illustrating the requirements analysis process for a multi-scenario vertical business system generation platform according to the present invention.

[0031] Figure 3 This is a genetic algorithm optimization diagram for a multi-scenario vertical business system generation platform of the present invention.

[0032] Figure 4 This is a flowchart illustrating the testing and correction process of a multi-scenario vertical business system generation platform according to the present invention. Detailed Implementation

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

[0034] This invention provides a multi-scenario vertical business system generation platform, comprising the following steps:

[0035] Step 1: Construct a multi-dimensional scenario meta-model library. The meta-model library contains scenario feature metadata, business process metadata, data structure metadata, and interface adaptation metadata for vertical business domains. The metadata is classified and indexed through a standardized tag system, which includes four core dimensions: scenario type, business complexity, data volume, and interface protocol type.

[0036] Step 2: Receive the scenario requirement parameters input by the user. The requirement parameters include business domain type, scenario functional requirements, performance indicator thresholds, external system integration requirements, and deployment environment constraints.

[0037] Step 3: Based on the requirement parsing mechanism combining natural language processing and rule engine, map the scenario requirement parameters to the target metadata set in the metamodel library mentioned in Step 1 to generate a requirement-metadata mapping relationship table; Step 4: Adopt a metadata combination optimization strategy based on genetic algorithm, select and combine suitable metadata according to the mapping relationship table to generate at least 3 sets of candidate business system frameworks. The fitness function of the genetic algorithm has the optimization objectives of business matching degree, development efficiency improvement rate, and system resource utilization rate;

[0038] Step 5: Quantitatively score the candidate business system frameworks using a multi-dimensional evaluation model. Evaluation dimensions include functional matching, scalability, compatibility, ease of deployment, and operation and maintenance costs, and select the optimal framework.

[0039] Step 6: Automatically generate core business code, configuration files, and deployment scripts based on the optimal framework. The core business code adopts a plug-in architecture design, which supports hot-swapping of functional modules.

[0040] Step 7: Build a dynamic simulation test environment to perform scenario-based stress testing, compatibility testing, and exception handling testing on the generated business system, and generate a test report;

[0041] Step 8: Automatically correct metadata combination deviations and code logic defects based on the test report. If the corrected system meets the preset indicator thresholds, output the complete business system package; otherwise, return to step 4 to re-optimize the metadata combination.

[0042] The standardized tagging system described in step 1 adopts a three-level tagging structure. The first-level tags are business domain classifications, the second-level tags are scenario feature subdivisions, and the third-level tags are technology adaptation attributes. The meta-model library supports user-defined tag expansion and dynamic updates of metadata.

[0043] The requirement parsing mechanism described in step 3 includes: first, using the BERT model to perform semantic understanding and keyword extraction on the natural language requirements input by the user; then, using a preset business rule base to perform structured transformation on the extracted keywords; and finally, establishing an accurate mapping with the metadata.

[0044] The optimization process of the genetic algorithm in step 4 includes: initializing the population as a random metadata combination scheme, retaining the top 60% of individuals by selecting operators, recombining the metadata modules of different individuals by crossover operators, randomly replacing some metadata modules by mutation operators, and iterating until the fitness values ​​converge or the preset number of iterations is reached.

[0045] The multi-dimensional evaluation model described in step 5 uses the analytic hierarchy process (AHP) to determine the weights of each evaluation dimension, with the following weights: functional matching degree (at least 30%), scalability (at least 20%), compatibility (at least 15%), deployment convenience (at least 15%), and operation and maintenance cost (at least 20%).

[0046] The plug-in architecture design described in step 6 includes a core framework layer, a plug-in interface layer, and a functional plug-in layer. The core framework layer provides the basic operating environment, the plug-in interface layer defines standardized interaction interfaces, and the functional plug-in layer includes business logic plug-ins, data processing plug-ins, and interface adaptation plug-ins. Each plug-in communicates with the core framework layer through the interface layer.

[0047] The dynamic simulation test environment described in step 7 supports automatic configuration of the test dataset size, number of concurrent users, and external dependent simulation services according to the business scenario. The number of concurrent users for stress testing can be dynamically adjusted within the range of 10-10000, and the test dataset size can be expanded by 1-10 times the business data volume.

[0048] It also includes step 9: establishing a full lifecycle management mechanism for business systems, including operational status monitoring, functional module upgrades, vulnerability patching and system expansion, and supporting automatic adjustment of system resource configuration according to business growth trends.

[0049] The meta-model library mentioned in step 1 aggregates vertical business data from multiple industries through a federated learning mechanism, achieving cross-domain optimization of metadata while ensuring data privacy. Furthermore, the metadata updates adopt an incremental update strategy, synchronizing only newly added and changed metadata information.

[0050] The automatically generated core business code described in step 6 supports output in multiple programming languages, including Java, Python, Go, and .NET, and the configuration file supports multiple formats such as YAML, JSON, and XML. The deployment script is compatible with Docker, Kubernetes, and physical machine deployment environments.

[0051] Take the creation of a "smart logistics and warehousing management system" as an example.

[0052] First, in step 1, the platform has pre-built a meta-model library in the logistics field, which includes metadata for business processes such as "inbound management", "inventory counting" and "outbound scheduling", as well as corresponding cargo data models, RFID interface adaptation and other metadata.

[0053] In step 2, the user (a logistics company technician) inputs the following requirements: "We need a warehouse management system that can handle 100,000 goods entering the warehouse per day, supports both RFID and barcode identification methods, and can interface with the existing ERP system (through HTTP API integration), and be deployed on a private cloud Kubernetes cluster."

[0054] In step 3, the platform's BERT model understands that "100,000 items per day" corresponds to a high data volume, "RFID and barcodes" corresponds to multiple identification technology interfaces, and "HTTPAPI" corresponds to specific interface protocol metadata. The rule engine structures this information and maps it to the corresponding metadata in the metamodel library.

[0055] In step 4, the genetic algorithm begins its work. The initial population is a random combination of various metadata. The fitness function evaluates each combination: a solution that only supports barcodes has a low business fit score; a solution with excessive resource consumption is also eliminated. Through iteration, the algorithm eventually outputs several candidate frames that balance functionality, efficiency, and resources.

[0056] In step 5, the evaluation model scores these frameworks. A framework that adopts a microservice pluggable architecture, supports two recognition technologies, and provides an ERP adapter plugin receives the highest score due to its high scalability and high compatibility, and is selected as the optimal framework.

[0057] In step 6, the platform generates core business code in Java (such as the database entry logic module), a Kubernetes deployment configuration file in YAML format, and a Docker image build script based on this framework. The code structure is clear, and RFID reading and barcode reading are designed as two independent, hot-swappable plugins.

[0058] In step 7, the dynamic simulation test environment is started. It simulates the peak inbound pressure of 120,000 pieces / day and simulates the latency and occasional failures of the ERP system interface, thus performing stress and anomaly tests on the production system.

[0059] In step 8, the test report showed that the system's response time slightly exceeded the standard under peak pressure. The platform automatically adjusted the metadata combination, optimized the metadata configuration of the database connection pool, and regenerated the code. The second test passed, and the platform finally output a complete, directly deployable "Smart Logistics Warehouse Management System" software package.

[0060] In another scenario where a financial risk control system is generated, the platform can learn risk control models and rule features from multiple partner financial institutions through the federated learning mechanism described in step 1, without accessing the original sensitive data, thereby optimizing its own risk control business process metadata and generating a more accurate and powerful risk control system.

[0061] The present invention and its embodiments have been described above. This description is not restrictive. In short, if a person skilled in the art is inspired by this description and designs a similar structure and embodiment without departing from the spirit of the present invention, such design should fall within the protection scope of the present invention.

Claims

1. A multi-scenario vertical business system generation platform, characterized in that, Includes the following steps: Step 1: Construct a multi-dimensional scenario meta-model library. The meta-model library contains scenario feature metadata, business process metadata, data structure metadata, and interface adaptation metadata for vertical business domains. The metadata is classified and indexed through a standardized tag system, which includes four core dimensions: scenario type, business complexity, data volume, and interface protocol type. Step 2: Receive the scenario requirement parameters input by the user. The requirement parameters include business domain type, scenario functional requirements, performance indicator thresholds, external system integration requirements, and deployment environment constraints. Step 3: Based on the requirement parsing mechanism combining natural language processing and rule engine, map the scenario requirement parameters to the target metadata set in the metamodel library mentioned in Step 1 to generate a requirement-metadata mapping relationship table; Step 4: Adopt a metadata combination optimization strategy based on genetic algorithm, select and combine suitable metadata according to the mapping relationship table to generate at least 3 sets of candidate business system frameworks. The fitness function of the genetic algorithm has the optimization objectives of business matching degree, development efficiency improvement rate, and system resource utilization rate; Step 5: Quantitatively score the candidate business system frameworks using a multi-dimensional evaluation model. Evaluation dimensions include functional matching, scalability, compatibility, ease of deployment, and operation and maintenance costs, and select the optimal framework. Step 6: Automatically generate core business code, configuration files, and deployment scripts based on the optimal framework. The core business code adopts a plug-in architecture design, which supports hot-swapping of functional modules. Step 7: Build a dynamic simulation test environment to perform scenario-based stress testing, compatibility testing, and exception handling testing on the generated business system, and generate a test report; Step 8: Automatically correct metadata combination deviations and code logic defects based on the test report. If the corrected system meets the preset indicator thresholds, output the complete business system package; otherwise, return to step 4 to re-optimize the metadata combination.

2. The multi-scenario vertical business system generation platform according to claim 1, characterized in that: The standardized tagging system described in step 1 adopts a three-level tagging structure. The first-level tags are business domain classifications, the second-level tags are scenario feature subdivisions, and the third-level tags are technology adaptation attributes. The meta-model library supports user-defined tag expansion and dynamic updates of metadata.

3. The multi-scenario vertical business system generation platform according to claim 1, characterized in that: The requirement parsing mechanism described in step 3 includes: first, using the BERT model to perform semantic understanding and keyword extraction on the natural language requirements input by the user; then, using a preset business rule base to perform structured transformation on the extracted keywords; and finally, establishing an accurate mapping with the metadata.

4. The multi-scenario vertical business system generation platform according to claim 1, characterized in that: The optimization process of the genetic algorithm in step 4 includes: initializing the population as a random metadata combination scheme, retaining the top 60% of individuals by selecting operators, recombining the metadata modules of different individuals by crossover operators, randomly replacing some metadata modules by mutation operators, and iterating until the fitness values ​​converge or the preset number of iterations is reached.

5. The multi-scenario vertical business system generation platform according to claim 1, characterized in that: The multi-dimensional evaluation model described in step 5 uses the analytic hierarchy process (AHP) to determine the weights of each evaluation dimension, with the following weights: functional matching degree (at least 30%), scalability (at least 20%), compatibility (at least 15%), deployment convenience (at least 15%), and operation and maintenance cost (at least 20%).

6. The multi-scenario vertical business system generation platform according to claim 1, characterized in that: The plug-in architecture design described in step 6 includes a core framework layer, a plug-in interface layer, and a functional plug-in layer. The core framework layer provides the basic operating environment, the plug-in interface layer defines standardized interaction interfaces, and the functional plug-in layer includes business logic plug-ins, data processing plug-ins, and interface adaptation plug-ins. Each plug-in communicates with the core framework layer through the interface layer.

7. The multi-scenario vertical business system generation platform according to claim 1, characterized in that: The dynamic simulation test environment described in step 7 supports automatic configuration of the test dataset size, number of concurrent users, and external dependent simulation services according to the business scenario. The number of concurrent users for stress testing can be dynamically adjusted within the range of 10-10000, and the test dataset size can be expanded by 1-10 times the business data volume.

8. The multi-scenario vertical business system generation platform according to claim 1, characterized in that: It also includes step 9: establishing a full lifecycle management mechanism for business systems, including operational status monitoring, functional module upgrades, vulnerability patching and system expansion, and supporting automatic adjustment of system resource configuration according to business growth trends.

9. A multi-scenario vertical business system generation platform according to claim 1, characterized in that: The meta-model library mentioned in step 1 aggregates vertical business data from multiple industries through a federated learning mechanism, achieving cross-domain optimization of metadata while ensuring data privacy. Furthermore, the metadata updates adopt an incremental update strategy, synchronizing only newly added and changed metadata information.

10. A multi-scenario vertical business system generation platform according to claim 1, characterized in that: The automatically generated core business code described in step 6 supports output in multiple programming languages, including Java, Python, Go, and .NET, and the configuration file supports multiple formats such as YAML, JSON, and XML. The deployment script is compatible with Docker, Kubernetes, and physical machine deployment environments.

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