Serverless low-code intelligent architecture generation system based on multi-modal input

Abstract

The invention provides a Serverless low-code intelligent architecture generation system based on multi-modal input. The problems that a traditional Serverless architecture is complex in design, a low-code platform is insufficient in flexibility, architecture design and implementation are disjointed and the like are solved. The system comprises a multi-modal input processing module, a Serverless architecture generation module, a low-code visual design module and a Serverless function automatic generation module. Various inputs such as texts, charts and code snippets can be processed at the same time, a Serverless software architecture is intelligently generated, a real-time adjustment interface is provided, and architecture design is automatically mapped to specific function implementation. The innovation point is that multi-modal input understanding, Serverless architecture design and a low-code development platform are integrated, and the whole-process intelligence from demand to deployment is realized. The method has the advantages that the development efficiency is improved, the threshold is reduced, the flexibility and the expandability are enhanced, and powerful support is provided for an enterprise to quickly construct and deploy Serverless applications.

Description

Technical Field

[0001] The present invention relates to the field of software engineering, and in particular to a software architecture design system based on artificial intelligence and cloud computing technology. Specifically, the present invention relates to a system for intelligent software architecture design and low-code application development in a serverless environment using multimodal input and machine learning algorithms. Background Art

[0002] Software architecture design and development is the core of software engineering, and is of great significance for improving software quality, reducing development costs, and shortening development cycles. With the rapid development of cloud computing technology, especially the rise of Serverless architecture, software design and development models are undergoing profound changes. In the current wave of digital transformation, efficient and flexible software development methods have important applications in enterprise informatization construction, Internet application development and other fields.

[0003] At present, software architecture design mainly adopts a combination of manual design and auxiliary design tools. Traditional software architecture design usually relies on the experience and expertise of architects, and uses modeling tools such as UML for visual design. In the development stage, the application of low-code platforms is becoming more and more widespread. Through visual interfaces and predefined components, non-professional developers can also quickly build applications. On the other hand, the emergence of Serverless technology simplifies infrastructure management, allowing developers to focus more on the implementation of business logic.

[0004] However, these solutions still have many problems when facing complex Serverless application development. Traditional architecture design methods are difficult to fully utilize the advantages of Serverless architecture, often resulting in inefficient resource utilization or inflexible architecture. Although existing low-code platforms have improved development efficiency, they are unable to cope with complex business logic and customized requirements. In addition, the separation between architecture design and specific implementation often leads to misunderstanding or loss of design intent during the development process, affecting the quality of the final product.

[0005] To solve the above problems, the industry has made some attempts to improve. For example, artificial intelligence technology is introduced to assist in architecture design, or design tools are developed specifically for serverless environments. Although these methods have improved efficiency in some aspects, they still have limitations. AI-assisted design tools often lack the ability to comprehensively understand multiple input forms and find it difficult to fully capture the designer's intentions. Specialized serverless design tools are often out of touch with existing development processes, increasing learning costs and integration difficulties.

[0006] Therefore, developing an integrated system that can intelligently understand multimodal input, automatically generate Serverless architecture, and support low-code development has become a research hotspot in this field. Such a system not only needs to have powerful intelligent analysis and generation capabilities, but also needs to be able to seamlessly connect the design and development stages, providing users with full-process support from concept to deployment. At the same time, how to provide sufficient flexibility while maintaining ease of use is also a key issue that needs to be solved urgently. Summary of the invention

[0007] The main purpose of this invention is to solve the limitations of existing Serverless low-code development platforms in dealing with complex business logic and architecture design, as well as the problem that traditional software architecture design methods are difficult to fully utilize the advantages of Serverless architecture. In addition, this invention also aims to bridge the gap between architecture design and specific implementation, and improve the overall efficiency and quality of software development.

[0008] To achieve the above objectives, the present invention provides a Serverless low-code intelligent architecture generation system based on multimodal input, which is characterized by: including a multimodal input processing module, a Serverless architecture generation module, a low-code visual design module and a Serverless function automatic generation module, realizing the full process intelligence from demand analysis to architecture design and then to specific implementation. In some embodiments, the system also integrates multi-cloud deployment functions to support seamless deployment of the generated Serverless architecture to different cloud service provider platforms.

[0009] Specifically, the multimodal input processing module of the present invention can simultaneously process various forms of input information such as text descriptions, chart sketches and code snippets, and comprehensively capture the user's design intention through multimodal data fusion, context understanding and Serverless knowledge graph construction.

[0010] Furthermore, the Serverless architecture generation module uses machine learning algorithms to generate an initial software architecture suitable for the Serverless environment based on processed input information, and can continuously optimize the architecture based on continuous input and feedback. This module includes a microservice division unit, an event-driven design unit, and a resource optimization unit, which can intelligently divide system functions into microservices suitable for Serverless deployment.

[0011] Preferably, the low-code visual design module provides an interface for real-time display of architecture changes, supports drag-and-drop operations for architecture adjustment, and can generate intelligent optimization suggestions. The module includes a component library management unit, a visual workflow design unit, and a real-time preview unit, so that even non-professionals can easily participate in the architecture design process.

[0012] Optionally, the Serverless function automatic generation module can automatically map the high-level architecture design to the Serverless function implementation, including functions such as code template selection, parameter configuration, and dependency management, greatly simplifying the conversion process from design to implementation.

[0013] In one embodiment, the system also includes a multi-cloud deployment module that supports seamless deployment of the generated Serverless architecture to different cloud service provider platforms and provides cross-cloud resource management and monitoring capabilities, thereby enhancing the practicality and flexibility of the system.

[0014] In some embodiments, the Serverless knowledge graph construction unit integrates a cloud service integration mechanism, which can obtain and integrate Serverless product information from major cloud service providers in real time, ensuring that the system can always utilize the latest Serverless technology.

[0015] In addition, the microservice partitioning unit of the present invention adopts a machine learning algorithm to optimize the partitioning of microservices by analyzing the dependencies and call frequencies of system functions, thereby improving the efficiency and maintainability of the Serverless architecture.

[0016] In other embodiments, the visual workflow design unit supports data flow and control flow between custom Serverless functions, and can automatically generate corresponding event trigger configurations, greatly enhancing the flexibility and expressiveness of the system.

[0017] In a preferred embodiment, the system implements intelligent selection of code templates through a deep learning model, and can automatically recommend the most suitable Serverless function implementation method based on the architectural design and functional requirements, greatly improving the quality and efficiency of code generation.

[0018] In another preferred embodiment, the system integrates cross-cloud resource management and monitoring functions, which not only supports multi-cloud deployment, but also provides a unified resource management interface and real-time performance monitoring, allowing developers to better optimize and manage distributed Serverless applications.

[0019] By adopting the above scheme, the present invention has the following beneficial effects: (1) The development efficiency of Serverless applications has been significantly improved, and the entire process from demand analysis to deployment has been intelligently supported; (2) It significantly lowers the threshold for serverless architecture design and development, allowing more non-professionals to participate in the development of complex applications; (3) The flexibility and scalability of the architecture design have been enhanced, and the system can quickly adjust and optimize the architecture according to changing requirements; (4) Achieved seamless transition from design intent to specific implementation, reducing problems caused by poor communication or misunderstanding; (5) Support for multi-cloud deployment improves application portability and reduces vendor lock-in risks.

[0020] In summary, the present invention provides powerful tool support for enterprises to quickly build and deploy high-quality Serverless applications by innovatively combining multimodal input understanding, intelligent Serverless architecture generation and low-code development platform, and is expected to promote technological progress in the field of Serverless computing and low-code development. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 This is the overall system architecture diagram. It shows the main modules of the entire system, including the multimodal input processing module, the serverless architecture generation module, the low-code visual design module, the serverless function automatic generation module, and their connection with the multi-cloud deployment module and the intelligent optimization module through the data bus.

[0023] Figure 2 It is a multimodal input processing diagram. It describes in detail the internal structure of the multimodal input processing module, including the multimodal data fusion unit, the context understanding unit, and the Serverless knowledge graph construction unit, as well as how they process different types of inputs and generate unified semantic representations.

[0024] Figure 3 This is a Serverless architecture generation diagram. It shows the workflow of the Serverless architecture generation module, including the microservice division unit, event-driven design unit, and resource optimization unit, and how they work together to generate an optimized Serverless architecture solution.

[0025] Figure 4 It is a low-code visual design diagram. It describes the composition of the low-code visual design module, including the component library management unit, the visual workflow design unit, and the real-time preview unit, and how they support users to perform visual Serverless application design.

[0026] Figure 5This is a diagram of the automatic generation of Serverless functions. It shows the internal structure of the automatic generation module of Serverless functions, including the code template selection unit, parameter configuration unit, and dependency management unit, and how they work together to generate the final Serverless function code.

[0027] Figure 6 It is a multi-cloud deployment diagram. It describes the composition of the multi-cloud deployment module, including the cloud platform adaptation unit, resource orchestration unit, and cross-cloud monitoring unit, and how they implement multi-cloud deployment and management of Serverless applications.

[0028] Figure 7 This is the intelligent optimization diagram. It shows the internal structure of the intelligent optimization module, including the performance analysis unit, code refactoring unit, and resource tuning unit, and how they work together to achieve continuous optimization of Serverless applications. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as a limitation on the present invention.

[0031] Example 1: System overall architecture. Figure 1 The serverless low-code intelligent architecture generation system 100 based on multimodal input provided by the present invention includes a multimodal input processing module 110, a serverless architecture generation module 120, a low-code visual design module 130, and a serverless function automatic generation module 140. These modules are interconnected through a data bus 150 to achieve efficient exchange of data and control information.

[0032] The multimodal input processing module 110 is the front-end interface of the system, responsible for receiving and processing various forms of user input. The module contains three core units: a multimodal data fusion unit 111, a context understanding unit 112, and a serverless knowledge graph construction unit 113. The multimodal data fusion unit 111 uses an innovative deep learning algorithm that can simultaneously process text descriptions, chart sketches, and code snippets, converting different forms of input into a unified semantic representation. The context understanding unit 112 uses advanced natural language processing technology to capture implicit context information in the input to ensure accurate understanding of user intent. The serverless knowledge graph construction unit 113 dynamically maintains a knowledge base containing the latest serverless technologies and best practices to provide support for subsequent architecture generation.

[0033] The serverless architecture generation module 120 is the core of the system and is responsible for generating a software architecture suitable for the serverless environment based on the processed input information. This module includes a microservice division unit 121, an event-driven design unit 122, and a resource optimization unit 123. The microservice division unit 121 uses an innovative graph neural network algorithm to automatically divide system functions into an optimal set of microservices by analyzing functional dependencies and call frequencies. The event-driven design unit 122 is responsible for designing event-driven interaction mechanisms between services to ensure the loose coupling and scalability of the architecture. The resource optimization unit 123 uses a reinforcement learning algorithm to automatically optimize the resource configuration and trigger settings of the serverless function based on preset performance indicators and cost constraints.

[0034] The low-code visual design module 130 provides an intuitive user interface that allows users to view and adjust the generated architecture in a visual way. The module includes a component library management unit 131, a visual workflow design unit 132, and a real-time preview unit 133. The component library management unit 131 maintains a rich library of predefined Serverless functional components to support users to quickly build complex applications. The visual workflow design unit 132 provides an innovative drag-and-drop interface that allows users to customize the data flow and control flow between Serverless functions, and can automatically generate corresponding event trigger configurations. The real-time preview unit 133 uses virtual DOM technology to display the impact of architectural changes on the overall system in real time, helping users make informed design decisions.

[0035] The Serverless function automatic generation module 140 is responsible for automatically converting the high-level architecture design into a specific Serverless function implementation. This module includes a code template selection unit 141, a parameter configuration unit 142, and a dependency management unit 143. The code template selection unit 141 uses an innovative deep learning model based on a graph attention network to intelligently recommend the most suitable Serverless function implementation method based on the architecture design and functional requirements. The parameter configuration unit 142 automatically sets the triggers, permissions, and environment variables of the Serverless function to ensure that the function can run correctly. The dependency management unit 143 is responsible for intelligently analyzing and packaging the dependency libraries required by the Serverless function, optimizing the cold start time and operating efficiency of the function.

[0036] The system also includes a multi-cloud deployment module (not shown in the figure) that supports seamless deployment of the generated Serverless architecture to different cloud service provider platforms. This module provides a unified resource management interface and real-time performance monitoring capabilities, enabling developers to better optimize and manage distributed Serverless applications.

[0037] In actual applications, users first input system requirements and design intentions through the multimodal input processing module 110. The system processes these inputs in real time and displays the preliminary architecture design on the interface of the low-code visual design module 130. Users can make adjustments through the visual interface, and the system will update the architecture design in real time and provide optimization suggestions. Once the user confirms the architecture design, the Serverless function automatic generation module 140 will start to generate specific function implementations. Throughout the process, the system will continue to learn and optimize to improve the quality and efficiency of architecture generation.

[0038] The innovation of this embodiment is to realize the full process intelligence from multimodal input to Serverless architecture design and then to specific function implementation. The system is not just a simple combination of existing technologies, but through innovative algorithms and architecture design, it realizes the deep integration and collaborative optimization of various modules. For example, the deep learning algorithm of the multimodal input processing module can accurately capture user intentions, the graph neural network algorithm of the Serverless architecture generation module can generate the optimal microservice division, and the graph attention network model of the Serverless function automatic generation module can achieve high-quality code generation. These innovations together constitute a new intelligent Serverless application development paradigm, which greatly improves development efficiency and application quality.

[0039] It should be noted that the specific implementation method described in this embodiment is only exemplary, and those skilled in the art can adjust and optimize the specific implementation of each module according to actual needs. As long as it does not deviate from the core idea of ​​the present invention, it should be regarded as the scope of protection of the present invention.

[0040] Example 2: Multimodal input processing. Figure 2 This embodiment describes in detail the specific implementation of the multimodal input processing module 110. This module is the front-end interface of the system and is responsible for processing various forms of user input, including but not limited to text descriptions, chart sketches, and code snippets.

[0041] The multimodal input processing module 110 mainly includes three core units: a multimodal data fusion unit 111, a context understanding unit 112, and a serverless knowledge graph construction unit 113. These units work together to convert the user's multimodal input into a unified semantic representation that the system can understand.

[0042] The multimodal data fusion unit 111 uses an innovative cross-modal attention mechanism to process different types of input data. The core idea of ​​this mechanism is to allow the features of each modality to learn and enhance each other. The specific implementation is as follows: (1) For text input, use the BERT (Bidirectional Encoder Representations from Transformers) model to encode and obtain text features T; (2) For the chart sketch, use the improved VGG (Visual Geometry Group) network to extract features and obtain image feature I; (3) For the code snippet, use the CodeBERT model to encode it and obtain the code feature C; (4) These three features are input into the cross-modal attention layer and calculated as follows: in, , , is a learnable weight matrix; (5) Then, the features are weighted fused using the attention weights: (6) Finally, , , Concatenate and pass through a fully connected layer to obtain the final fusion feature F.

[0043] The context understanding unit 112 uses an advanced graph attention network (GAT) to capture implicit context information in the input. The workflow of this unit is as follows: (1) Use the fused feature F as the initial feature of the node of the graph; (2) Build a fully connected graph where each node represents an element in the input (such as a sentence, a graphical element, or a piece of code); (3) Use GAT to perform multiple rounds of information dissemination and update node features: in, is the attention coefficient, W is the learnable weight matrix, and σ is the activation function; (4) After multiple rounds of propagation, the features of each node contain global context information.

[0044] The Serverless Knowledge Graph Building Unit 113 is an innovation of this module. It dynamically maintains a knowledge base containing the latest Serverless technologies and best practices. The implementation of this unit includes the following steps: (1) Use web crawlers to collect serverless product information and best practice documents from major cloud service providers in real time; (2) Extract entities and relations from collected documents using natural language processing techniques; (3) Build an initial knowledge graph, where nodes represent serverless concepts and components, and edges represent the relationships between them; (4) Using graph embedding techniques (such as TransE) to learn low-dimensional representations of knowledge graphs; (5) Match user input with the knowledge graph to find relevant Serverless concepts and best practices; (6) The matching results are integrated with the previously obtained context features to obtain the final semantic representation.

[0045] An important innovation of this embodiment is the deep integration and understanding of multimodal input. Through the cross-modal attention mechanism, the system can make full use of the complementary information between different modalities and improve the accuracy of understanding user intentions. For example, in a test case, when the user provides the text description "implementing the user authentication function", the authentication flowchart and some code snippets at the same time, the system can accurately identify that the user wants to implement a stateless authentication mechanism based on JWT (JSON Web Token), and automatically introduce related Serverless components in the subsequent architecture design.

[0046] In addition, the dynamically updated Serverless knowledge graph enables the system to always take advantage of the latest Serverless technologies and best practices. In practical applications, this feature ensures that the architecture solutions generated by the system always remain advanced and feasible. For example, when a cloud service provider launches a new Serverless database service, the system can quickly take it into consideration and recommend its use in appropriate scenarios.

[0047] It should be noted that the above implementation is only exemplary. Those skilled in the art can adjust and optimize the algorithms and models according to actual needs. For example, other advanced pre-trained models can be used to replace BERT and VGG, or different graph neural network structures can be used to achieve context understanding. As long as they do not deviate from the core idea of ​​multimodal input processing, these changes should be regarded as the scope of protection of the present invention.

[0048] Example 3: Serverless architecture generation. Figure 3 This embodiment describes in detail the specific implementation of the Serverless architecture generation module 120. This module is the core of the system and is responsible for generating a software architecture suitable for the Serverless environment based on the processed input information.

[0049] The serverless architecture generation module 120 mainly includes a microservice partitioning unit 121, an event-driven design unit 122, and a resource optimization unit 123. These units work together to generate an optimized serverless architecture solution.

[0050] The microservice partitioning unit 121 uses a combination of graph neural network (GNN) and spectral clustering to achieve automated microservice partitioning. The system first constructs an initial functional dependency graph, where nodes represent functional modules, edges represent call relationships, and edge weights represent call frequencies. Then, the graph attention network (GAT) is used to learn node representations to capture the complex relationships between functional modules. The node feature update formula is: ,in is the attention coefficient and W is the learnable weight matrix.

[0051] The learned node representations are used for spectral clustering, which involves constructing a similarity matrix S ( ), calculate the Laplacian matrix (D is the degree matrix), solve the generalized eigenvalue problem , select the eigenvectors corresponding to the k smallest non-zero eigenvalues, and perform K-means clustering on these eigenvectors. Finally, the system uses modularity metrics to evaluate the quality of the partitioning and applies a local search algorithm for fine-tuning.

[0052] The event-driven design unit 122 designs event-driven interaction mechanisms between services. This unit analyzes data flows between microservices, identifies key events, defines event structures, designs event buses, and generates code templates for event publishing and subscription for each microservice. The system ensures idempotence and replayability of events, and designs retry and error handling mechanisms for event processing.

[0053] The resource optimization unit 123 uses a reinforcement learning algorithm to optimize the resource configuration and trigger settings of the Serverless function. The state space includes the memory configuration, concurrency settings, trigger types, etc. of the function, and the action space includes adjusting memory, modifying concurrency limits, changing trigger settings, etc. The reward function is defined as: , where w1 and w2 are weight coefficients. The system uses a deep Q network (DQN) to learn the optimal strategy, and the state transition formula is: Through experience replay and target network stabilization training process, the learned policy is used to periodically optimize resource allocation.

[0054] The innovation of this embodiment is to combine graph neural network and spectral clustering to achieve intelligent and accurate microservice partitioning, and use reinforcement learning to achieve dynamic optimization of Serverless resources. These methods improve the maintainability, scalability and performance of the system, while optimizing resource utilization efficiency.

[0055] It should be noted that the above implementation is only exemplary. Those skilled in the art can adjust and optimize the algorithm and model according to actual needs. As long as they do not deviate from the core idea of ​​intelligent generation and optimization of Serverless architecture, these changes should be regarded as the scope of protection of the present invention.

[0056] Example 4: Low-code visual design. Figure 4 , this embodiment describes in detail the specific implementation of the low-code visual design module 130. The module provides an intuitive user interface that allows users to view and adjust the generated Serverless architecture in a visual manner.

[0057] The low-code visual design module 130 mainly includes a component library management unit 131, a visual workflow design unit 132, and a real-time preview unit 133. These units work together to provide users with a powerful and easy-to-use Serverless application design environment.

[0058] The component library management unit 131 maintains a rich library of predefined Serverless functional components. These components include common Serverless function templates, database connectors, API gateway configurations, etc. The component library adopts a hierarchical structure, including a basic component layer, a business component layer, and a template layer. The basic component layer contains basic Serverless functions and event triggers; the business component layer contains functional modules for specific fields, such as user authentication, payment processing, etc.; the template layer provides complete application architecture templates, such as microservice architecture, event-driven architecture, etc.

[0059] The component library management unit 131 also implements an intelligent recommendation system to recommend the most suitable components based on the user's design context and historical usage data. The recommendation algorithm uses a method combining collaborative filtering and content-based methods, and its core formula is: in, is the collaborative filtering score based on user u and item i, is the content-based similarity score and α is the balancing factor.

[0060] The visual workflow design unit 132 is the core of this module. It provides an innovative drag-and-drop interface that allows users to customize the data flow and control flow between Serverless functions. This unit uses a directed acyclic graph (DAG) to represent the workflow. Nodes represent Serverless functions or event sources, and edges represent data flows or trigger relationships. Users can add, delete, or connect nodes by simply dragging and dropping, and the system will automatically generate the corresponding event trigger configuration and communication code between functions.

[0061] In order to ensure the correctness and efficiency of the design, the visual workflow design unit 132 implements a series of intelligent auxiliary functions: Automatic layout: Use force-directed algorithms to achieve automatic layout of graphs to ensure visual clarity; Cycle detection: detects cyclic dependencies in workflows in real time and provides warnings and suggestions; Performance analysis: Estimate the execution time and cost of workflows based on static analysis and historical data, and provide optimization suggestions; Version Control: Integrate the Git version control system to support version management and collaborative editing of workflows.

[0062] The real-time preview unit 133 uses virtual DOM technology to display the impact of architecture changes on the overall system in real time. When the user operates in the visual interface, the real-time preview unit will immediately update the logical view, physical view and deployment view of the system. The logical view shows the relationship between functional modules, the physical view shows the actual Serverless function and resource configuration, and the deployment view shows the deployment status in the cloud environment.

[0063] The real-time preview unit 133 also implements an innovative "What-If" analysis function. Users can simulate different input conditions or load conditions, and the system will calculate and display possible execution paths, resource consumption, and performance indicators in real time. This function uses the Monte Carlo simulation method, combined with historical performance data and resource usage models, to generate accurate prediction results.

[0064] An important innovation of this embodiment is the seamless combination of low-code development concepts and Serverless architecture design. Traditional Serverless development often requires a deep understanding of the specific APIs and configuration options of cloud service providers, while this system greatly reduces the development threshold through an intuitive visual interface and intelligent component recommendations. At the same time, the system retains sufficient flexibility to allow advanced users to perform in-depth customization and optimization.

[0065] Another innovation is the real-time preview and "What-If" analysis function, which enables users to have a clear understanding of the system's behavior and performance during the development phase, so as to make more extensive and reliable design decisions. This not only speeds up the development process, but also helps to improve the quality and reliability of the final system.

[0066] It should be noted that the above implementation is only exemplary. Those skilled in the art can adjust and optimize the interface design and function implementation according to actual needs. As long as they do not deviate from the core idea of ​​low-code visualization Serverless design, these changes should be regarded as the scope of protection of the present invention.

[0067] Example 5: Automatically generate serverless functions. Figure 5 This embodiment describes in detail the specific implementation of the Serverless function automatic generation module 140. This module is responsible for automatically converting the high-level architecture design into a specific Serverless function implementation, and is a key link in the implementation of this system from design to code.

[0068] The serverless function automatic generation module 140 mainly includes a code template selection unit 141, a parameter configuration unit 142, and a dependency management unit 143. These units work together to convert the abstract architecture design into a serverless function code that can be directly deployed.

[0069] The code template selection unit 141 uses an innovative deep learning model based on a graph attention network to intelligently recommend the most suitable Serverless function implementation method based on the architecture design and functional requirements. The model represents the architecture design as a graph structure, where nodes represent functional modules and edges represent the relationship between modules. The core of the model is a graph attention layer, whose update rules are as follows: in, is the feature vector of node i, is the attention coefficient, W is the learnable weight matrix, and σ is the nonlinear activation function. Through the multi-layer graph attention network, the model is able to capture complex architectural features.

[0070] The output layer of the model uses a softmax function to generate a probability distribution for each possible code template. The system selects the template with the highest probability and further customizes it as needed. This approach not only considers the characteristics of a single functional module, but also captures the interaction between modules, thereby generating a more appropriate and optimized code implementation.

[0071] The parameter configuration unit 142 is responsible for automatically setting the triggers, permissions, and environment variables of the Serverless function to ensure that the function can run correctly. This unit implements a rule engine that automatically infers the optimal configuration parameters based on a predefined best practice rule set and a machine learning model.

[0072] The rule engine uses a forward-chaining algorithm to infer the appropriate configuration from known facts, such as the purpose of the function, expected load, etc. For example, for functions that need to handle a large number of concurrent requests, the system automatically increases the concurrent execution limit and memory allocation. At the same time, the machine learning model continuously optimizes and updates these rules based on historical configuration data and performance indicators.

[0073] The dependency management unit 143 is responsible for intelligently analyzing and packaging the dependency libraries required by the Serverless function, optimizing the cold start time and operating efficiency of the function. This unit implements the following innovative functions: Dependency graph analysis: Use static code analysis techniques to build accurate dependency graphs and avoid including unnecessary libraries; Intelligent pruning: Automatically prunes unused code and dependencies based on the actual function calls to reduce the size of the deployment package. Layer optimization: Automatically move shared large dependency libraries to the Lambda layer or similar mechanisms, so that multiple functions can share them and reduce cold start time; Version compatibility check: automatically check the version compatibility between dependent libraries to avoid runtime conflicts; Security vulnerability scanning: Integrate with vulnerability database to automatically detect and report known security issues in dependent libraries.

[0074] This module also implements a code generation pipeline that integrates the selected template, configuration parameters, and optimized dependency information to generate the final Serverless function code. The generated code not only contains the main business logic, but also automatically includes best practices such as logging, error handling, and performance monitoring.

[0075] An important innovation of this embodiment is the use of graph attention networks to implement intelligent code template selection. This method can better understand and utilize the overall structure of the architecture and generate more optimized and consistent code implementations. Compared with traditional rule-based or simple machine learning methods, this system can handle more complex architecture designs and generate higher quality code.

[0076] Another innovation is intelligent dependency management. Through precise dependency analysis and intelligent optimization, the system can significantly reduce the size of the deployment package of the Serverless function, reduce the cold start time, and improve operational efficiency. This is crucial for the performance and cost optimization of Serverless applications.

[0077] It should be noted that the above implementation is only exemplary. Those skilled in the art can adjust and optimize the algorithm and function implementation according to actual needs. As long as they do not deviate from the core idea of ​​intelligent generation of Serverless functions, these changes should be regarded as the scope of protection of the present invention.

[0078] Example 6: Multi-cloud deployment. Figure 6 , this embodiment describes in detail the specific implementation of the multi-cloud deployment module 150. The module supports seamless deployment of the generated Serverless architecture to different cloud service provider platforms and provides cross-cloud resource management and monitoring functions.

[0079] The multi-cloud deployment module 150 mainly includes a cloud platform adaptation unit 151, a resource orchestration unit 152, and a cross-cloud monitoring unit 153. These units work together to achieve multi-cloud deployment and unified management of Serverless applications.

[0080] The cloud platform adaptation unit 151 is responsible for converting the general Serverless architecture description generated by the system into the deployment configuration of a specific cloud platform. This unit implements an extensible adapter framework that supports mainstream cloud service providers (such as AWS, Azure, Google Cloud) and open source Serverless platforms (such as OpenFaaS, Knative). The adaptation process includes the following steps: Resource mapping: mapping generic resource descriptions (e.g., functions, event sources, data stores) to specific services on the target platform; Configuration transformation: convert common configuration parameters into platform-specific configuration options; Permission translation: Convert the system-defined permission model into the IAM policy or similar mechanism of the target platform; API adaptation: Generate deployment scripts or configuration files that are compatible with the target platform API.

[0081] In order to handle the differences between different platforms, the cloud platform adaptation unit 151 uses a reasoning system based on a knowledge graph. The system maintains a knowledge base containing the service characteristics and best practices of each cloud platform, and implements an intelligent adaptation process through semantic reasoning.

[0082] The resource orchestration unit 152 is responsible for managing the resource allocation and deployment process across cloud environments. This unit implements a distributed state machine to coordinate complex multi-cloud deployment processes. The main functions include: Dependency analysis: Analyze the dependencies between application components and determine the optimal deployment order; Parallel deployment: maximize parallel deployment to reduce total deployment time while satisfying dependencies; Rollback mechanism: Automatically roll back when deployment fails to ensure system consistency; Version control: manage different versions for multiple environments (such as development, testing, and production).

[0083] The resource scheduling unit 152 also implements an innovative dynamic resource allocation algorithm. The algorithm dynamically adjusts the resource allocation strategy based on the application's performance requirements, cost budget, and real-time pricing information of each cloud platform. The core of the algorithm is a multi-objective optimization problem, which can be expressed as: in, Represents different optimization goals (such as performance, cost, reliability), and Represents the constraint condition. The algorithm uses the improved NSGA-II (Non-dominated Sorting Genetic Algorithm II) to solve this multi-objective optimization problem and generate a set of Pareto optimal solutions for users to choose.

[0084] The cross-cloud monitoring unit 153 provides a unified resource management interface and real-time performance monitoring capabilities. The unit implements the following innovative features: Unified indicator model: defines a set of standardized performance indicators and health status indicators that are applicable to different cloud platforms; Real-time data aggregation: Use distributed stream processing technology to collect and aggregate monitoring data from multiple cloud platforms in real time; Intelligent alert system: anomaly detection algorithm based on machine learning to identify potential performance issues and security threats; Automated repair: Combine predefined repair strategies and reinforcement learning algorithms to automatically repair certain common problems.

[0085] The core of the cross-cloud monitoring unit 153 is a prediction model based on time series analysis, which is used to predict resource usage trends and potential performance bottlenecks. The model uses the LSTM (Long Short-Term Memory Network) structure to capture long-term dependencies. The prediction formula is as follows: in, is the predicted output, is the hidden state, are input features, W and b are learnable parameters.

[0086] An important innovation of this embodiment is the realization of true multi-cloud Serverless deployment and management. Through intelligent platform adaptation and resource orchestration, the system can fully utilize the advantages of different cloud platforms while avoiding vendor lock-in. This provides enterprises with greater flexibility and bargaining power.

[0087] Another innovation is intelligent monitoring and optimization based on machine learning. Through predictive analysis and automated repair, the system can proactively identify and resolve potential problems, significantly improving the reliability and performance of Serverless applications.

[0088] It should be noted that the above implementation is only exemplary. Those skilled in the art can adjust and optimize the algorithm and function implementation according to actual needs. As long as they do not deviate from the core idea of ​​multi-cloud Serverless deployment and management, these changes should be regarded as the scope of protection of the present invention.

[0089] Example 7: Intelligent Optimization. Figure 7 This embodiment describes in detail the specific implementation of the intelligent optimization module 160. This module is one of the core innovations of the system and is responsible for continuously optimizing and improving the generated Serverless architecture and function implementation.

[0090] The intelligent optimization module 160 mainly includes a performance analysis unit 161, a code reconstruction unit 162, and a resource tuning unit 163. These units work together to achieve all-round intelligent optimization of Serverless applications.

[0091] The performance analysis unit 161 is responsible for collecting and analyzing the runtime performance data of the Serverless function. This unit implements a low-overhead distributed tracing system that can capture detailed information about function execution, including execution time, memory usage, CPU utilization, network I / O, etc. In order to minimize the impact on function performance, the tracing system adopts sampling and adaptive collection strategies.

[0092] The core of the performance analysis unit 161 is a performance bottleneck identification model based on a graph neural network. The model represents the serverless application as a function call graph, where nodes represent functions and edges represent call relationships. The update rules of the model are as follows: in, is the feature vector of node i, is the performance indicator vector of node j, is the attention coefficient, W is the learnable weight matrix, σ is the nonlinear activation function, and || represents vector concatenation. Through multi-layer graph convolution, the model can automatically identify performance bottlenecks and abnormal patterns.

[0093] The code refactoring unit 162 is responsible for automatically generating code refactoring suggestions based on performance analysis results and best practices. The unit implements a code analysis framework based on the program dependency graph (PDG) that can accurately capture the control flow and data flow of the code. The refactoring process includes the following steps: Code parsing: parse the function code into an abstract syntax tree (AST) and PDG; Pattern matching: Apply predefined refactoring rules on PDG to identify optimizable code patterns; Reconstruction generation: Generate a specific sequence of reconstruction operations based on the matching pattern; Impact analysis: Evaluate the potential impact of refactoring on performance, readability, and maintainability.

[0094] The code refactoring unit 162 also integrates a refactoring strategy generator based on deep reinforcement learning. The generator models the code refactoring problem as a Markov decision process (MDP) and learns the optimal refactoring strategy through interaction with the simulation environment. The reinforcement learning algorithm uses an improved PPO (Proximal Policy Optimization), and its objective function is: in, is the probability ratio of the new and old strategies, is the advantage function estimate and ε is the clipping parameter.

[0095] The resource tuning unit 163 is responsible for optimizing the resource configuration of the Serverless function, including memory allocation, concurrency settings, timeouts, etc. This unit implements a multi-objective Bayesian optimization framework that can find the best balance between performance, cost, and reliability. The optimization problem can be expressed as: Among them, P(x), C(x) and R(x) represent the performance, cost and reliability objective functions respectively. , and is the weight coefficient, is a constraint. Bayesian optimization uses Gaussian process as a proxy model and gradually approaches the global optimal solution through iterative sampling and updating.

[0096] Resource Tuning Unit 163 also implements an innovative adaptive resource allocation mechanism. This mechanism dynamically adjusts resource configuration based on the historical call pattern of the function and the real-time load situation. The core algorithm is a time series prediction model that combines ARIMA (Autoregressive Integrated Moving Average) and LSTM (Long Short-Term Memory Network) to accurately predict short-term and long-term resource demand trends.

[0097] An important innovation of this embodiment is the realization of all-round intelligent optimization of Serverless applications. By combining static code analysis, dynamic performance tracking and machine learning technology, the system can continuously optimize the performance, cost and reliability of applications, far exceeding traditional manual optimization methods.

[0098] Another innovation is the use of a variety of advanced machine learning and optimization algorithms, such as neural networks, deep reinforcement learning, and Bayesian optimization. These algorithms can handle the complexity and dynamics of the Serverless environment and provide more accurate and efficient optimization strategies.

[0099] It should be noted that the above implementation is only exemplary. Those skilled in the art can adjust and optimize the algorithm and function implementation according to actual needs. As long as they do not deviate from the core idea of ​​intelligent optimization of Serverless applications, these changes should be regarded as the scope of protection of the present invention.

[0100] Example 8: Comprehensive application. This example demonstrates the comprehensive application of the Serverless low-code intelligent architecture generation system based on multimodal input in practical applications. We will use the development process of an e-commerce platform to explain in detail how the various modules of the system work together, from demand input to final deployment.

[0101] In the initial demand input stage, users proposed the need to build an e-commerce platform with user authentication, product management, order processing, and payment functions through text descriptions, simple flowcharts, and some sample code snippets. The multimodal input processing module receives these inputs and uses BERT, improved VGG network, and CodeBERT models to process text, images, and codes respectively. The cross-modal attention mechanism fuses this information to generate a unified semantic representation. At the same time, the Serverless knowledge graph construction unit matches the input with existing Serverless best practices to provide guidance for subsequent architecture design.

[0102] The serverless architecture generation module starts working based on the processed input. The microservice division unit uses the graph neural network algorithm to divide the system functions into four main microservices: user service, product service, order service, and payment service. The event-driven design unit then designs the event-driven interaction mechanism between services, such as the "order creation" event triggering the payment process. The resource optimization unit preliminarily configures the resource parameters of each service, taking into account the high concurrency characteristics of the e-commerce platform.

[0103] The generated initial architecture is presented in the low-code visual design module. Users can fine-tune the service structure through the drag-and-drop interface, such as adding a product recommendation service. The visual workflow design unit allows users to define detailed business processes, such as the processing flow of order status changes. The real-time preview unit continuously displays the logical view, physical view, and deployment view of the architecture, allowing users to intuitively understand the system structure.

[0104] Based on the adjusted architecture, the Serverless function automatic generation module starts working. The code template selection unit selects the appropriate function template for each microservice, such as selecting a template that includes JWT authentication for the user service. The parameter configuration unit automatically sets the trigger, permissions, and environment variables of the function, while the dependency management unit analyzes and optimizes the dependencies of each function, such as moving the shared authentication library to the Lambda layer.

[0105] The multi-cloud deployment module then performs the deployment operation. The cloud platform adapter unit converts the generated architecture into the configuration of AWS Lambda and Azure Functions. The resource orchestration unit coordinates the deployment process to ensure that the services are deployed on both cloud platforms in the correct order. The cross-cloud monitoring unit sets up a unified monitoring panel to display performance indicators on both platforms in real time.

[0106] After deployment, the intelligent optimization module begins the continuous optimization process. The performance analysis unit collects runtime data and finds that the order service has performance bottlenecks during peak hours. The code refactoring unit analyzes the code of the order service and recommends optimizing database operations to batch mode. The resource tuning unit dynamically adjusts the memory configuration and concurrency limit of the order service to cope with traffic fluctuations.

[0107] Through this comprehensive application example, we can see how the system can generate an optimized and deployable Serverless e-commerce platform from the initial vague requirements through the collaborative work of multiple intelligent modules. Each module of the system plays a role within its own scope of responsibility, while working closely together to form a complete intelligent Serverless development process.

[0108] This example demonstrates several key advantages of this system, including multi-modal input understanding capabilities, intelligent architecture design and optimization, low-code visual interface, automated code generation and deployment, as well as multi-cloud support and intelligent optimization. These features together ensure the best performance and development efficiency of applications in complex environments.

[0109] It should be noted that this is just an example of system application. The scope of application of the present invention is far from limited to e-commerce platforms. It can be used for various complex Serverless application development, such as IoT data processing, real-time analysis systems, AI service platforms, etc. The modular design and scalability of the system enable it to adapt to the needs of different fields and provide comprehensive intelligent support for Serverless application development.

Claims

1. A Serverless low-code intelligent architecture generation system based on multimodal input, characterized in that: include: A multimodal input processing module that processes input information of text descriptions, diagram sketches, and code snippets simultaneously; The serverless architecture generation module is used to generate the initial software architecture suitable for the serverless environment based on the processed input information, and continuously optimize the architecture based on continuous input and feedback; A low-code visual design module that displays architecture changes in real time, provides a drag-and-drop interface for architecture adjustments, and generates intelligent optimization suggestions; The Serverless function automatic generation module is used to automatically map high-level architecture design to Serverless function implementation.

2. The system according to claim 1, characterized in that The multimodal input processing module comprises: Multimodal data fusion unit, used to integrate different forms of input information; A context understanding unit, which is used to capture and utilize implicit contextual information in the input; The serverless knowledge graph construction unit is used to dynamically build a knowledge graph specific to the serverless architecture based on multimodal input.

3. The system according to claim 1, characterized in that The Serverless architecture generation module includes: Microservice division unit, used to intelligently divide system functions into microservices suitable for Serverless deployment; Event-driven design unit, used to design event-driven interaction mechanisms between services; Resource optimization unit, used to optimize the resource configuration and trigger settings of Serverless functions.

4. The system according to claim 1, characterized in that The low-code visual design module includes: Component library management unit, used to manage and display predefined Serverless functional components; Visual workflow design unit, used to design the execution process of Serverless functions by dragging and dropping; A real-time preview unit is used to show the impact of design changes on the overall architecture in real time.

5. The system according to claim 1, characterized in that The Serverless function automatic generation module includes: The code template selection unit is used to select an appropriate Serverless function code template based on the architecture design; Parameter configuration unit, which is used to automatically set triggers, permissions, and environment variables for Serverless functions; Dependency management unit, used to manage and package the dependency libraries required by Serverless functions.

6. The system according to claim 2, characterized in that The Serverless knowledge graph construction unit also includes a cloud service integration mechanism for real-time acquisition and integration of Serverless product information from major cloud service providers.

7. The system according to claim 3, characterized in that The microservice partitioning unit uses a machine learning algorithm to optimize the partitioning of microservices by analyzing the dependencies and calling frequencies of system functions.

8. The system according to claim 4, characterized in that The visual workflow design unit supports customizing data flows and control flows between Serverless functions, and can automatically generate corresponding event trigger configurations.

9. The system according to claim 5, characterized in that The code template selection unit uses a deep learning-based model and can intelligently recommend the most suitable Serverless function implementation method based on architecture design and functional requirements.

10. The system according to any one of claims 1 to 9, characterized in that: It also includes a multi-cloud deployment module to support seamless deployment of the generated Serverless architecture to different cloud service provider platforms and provide cross-cloud resource management and monitoring capabilities.

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