Process management method, system and equipment for large model service and medium

Abstract

The invention provides a process management method, system and device for large model service and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that a front-end and back-end separation architecture and a micro-service architecture are adopted, and a containerization technology is combined for system building; on the basis of the built system, designing various types of function nodes for building a large model service process; based on the designed function nodes and the built system, selection, association and attribute setting of the function nodes are carried out in a graphical interface by dragging the function nodes, so that a large model service process is built; performing file management and version management on the constructed large model service process; performing process testing and optimization on the constructed large model service process; and storing process data of the large model service process by adopting a distributed cache and database technology, and carrying out encrypted transmission on the process data. According to the method, the large model service process can be customized and arranged according to needs, so that efficient management and flexible application of the large model service are realized.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence technology, and more specifically relates to a process management method, system, device and medium for large model services. Background Technology

[0002] With the rapid development of artificial intelligence technology, large-scale model services, with their powerful language understanding, generation capabilities, and data analysis and processing capabilities, have been widely used in many fields such as natural language processing, intelligent customer service, content creation, and data analysis. They can efficiently process massive amounts of data, providing users with intelligent decision support and automated services, greatly improving work efficiency and service quality, and becoming a key technology driving the intelligent transformation of various industries. However, existing large model services have significant limitations in practical applications. Due to the vast differences in business scenarios, data characteristics, and functional requirements among different users, generalized large model services cannot accurately adapt to the specific needs of each user, lacking flexibility and customization capabilities. As a result, the service effect cannot fully meet user expectations, limiting the in-depth application of large models in more segmented scenarios. To address the lack of flexibility in large model services, the industry has begun exploring technical solutions for custom orchestration of large model services, hoping to build personalized service processes by flexibly combining different large model functional modules. However, current custom orchestration solutions generally suffer from several problems: on the one hand, their functions are relatively limited, only enabling simple model combinations and process connections, failing to meet the diverse functional requirements of complex business scenarios; on the other hand, the operation processes are cumbersome and complex, requiring users to have professional programming and algorithm knowledge, increasing the barrier to entry and implementation difficulty; furthermore, these solutions have poor maintainability, making it difficult to quickly adjust processes and expand functions when business requirements change or systems are upgraded, and their support for domain-specific large models is limited, making it difficult to adapt to the special business logic and data processing needs of different industries, resulting in a complex usage process and failing to truly achieve efficient, flexible, and scalable large model service process management. Summary of the Invention

[0003] To address the above problems, the present invention aims to provide a method, system, device, and medium for managing large model services. By providing multiple node types and custom orchestration functions, users can customize and orchestrate large model service processes as needed, thereby achieving efficient management and flexible application of large model services.

[0004] To achieve the above objectives, the present invention employs the following technical solution: In a first aspect, embodiments of this application provide a process management method for large model services, including: The system is built using a front-end / back-end separation architecture and a microservice architecture, combined with containerization technology. Based on the system, various types of functional nodes are designed to construct large model service processes; the functional nodes include input nodes, large model nodes, judgment nodes, URL nodes, knowledge base nodes, output nodes, and nodes for importing orchestrated processes. Based on the designed functional nodes and the built system, functional nodes can be selected, associated, and their attributes set by dragging and dropping in the graphical interface to build a large model service process. Perform file and version management on the large model service process that is built; Perform process testing and optimization on the constructed large-scale model service process; Distributed caching and database technology are used to store process data of large model service processes, and the process data is transmitted in encrypted form.

[0005] In an optional implementation, the system is built using a front-end / back-end separation architecture and a microservice architecture, combined with containerization technology, including: The system adopts a front-end and back-end separation architecture. The front-end uses the Angular framework and g6 components to implement interface display and process visualization, while the back-end uses the Spring Boot framework to process data and manage processes. The large model module is broken down into microservices. The Spring Cloud framework is used to implement the registration, discovery and governance functions of the microservices, and the microservices are packaged into container images using Kubernetes containerization technology.

[0006] In an optional implementation, the input node is used to receive information, data, or instructions input by the user, and generate text or attachments as the starting point of the process; The large model node is used to integrate and run large industry models in the domain, perform reasoning and prediction on user input, and generate answers and data results based on the results. The judgment node is used to judge the user input or the output of the previous functional node according to preset conditions, and determine the execution path of the subsequent process. The URL node is used to access and invoke external resources or services and obtain relevant information; The knowledge base node is used to store and manage knowledge base information for specific domains and industries, and to provide data support and information integration for the large model node. The output node is used to generate the processing result and output the processing result in the form of text, voice or image, as the end point of the entire process; The "Import Arranged Process Nodes" is used to import and copy pre-arranged large model processes.

[0007] In an optional implementation, the system based on the designed functional nodes allows for the selection, association, and attribute setting of functional nodes through drag-and-drop functionality in a graphical interface, thereby constructing a large model service process, including: Based on the designed functional nodes and the built system, different functional node types can be selected by dragging and dropping functional nodes in the graphical interface, and connected in a specific order and logical relationship to build a large model service process that meets the requirements. Configure the input parameters, output parameters, calling conditions, and running conditions for each functional node.

[0008] In an optional implementation, the file management and version management of the constructed large model service process includes: After completing the construction of the large model service process, the functional node information, connection relationship and attribute parameters of the process are serialized to generate process files, which are then stored in a distributed database, and a unique identifier is generated for each process file. Select a saved process file through the process list interface, perform a deserialization operation on the process file, restore the process to the arrangement interface, and then edit or load the process. Perform node addition / deletion, connection relationship adjustment, or parameter modification operations on the loaded large model service process, and save the modification record in real time; Select the process files that are no longer needed and delete them to completely remove the process files and their related data from the distributed database; When a process file is saved, a version number is automatically generated for that process, and the creation time and modifications of that process file version are recorded.

[0009] In an optional implementation, the process testing and optimization of the constructed large model service process includes: Create simulation data, perform functional and performance tests on the large model service process using Postman and JMeter tools, and use the monitoring tool Prometheus to monitor the running data in real time; Adjust the functional node parameters and connection logic based on the test results.

[0010] In an optional implementation, the process data of the large model service flow stored using distributed caching and database technology, and the process data transmitted in encrypted form, includes: Distributed caching and database technologies are used to store user input and output data as well as process execution logs; User input is validated and filtered, and data is transmitted using the HTTPS encryption protocol; When a microservice or container fails, it is automatically detected and switched to a standby instance to continue running.

[0011] Secondly, embodiments of this application also provide a process management system for large model services, including: The technical architecture building module is used to build systems using a front-end / back-end separation architecture and a microservice architecture, combined with containerization technology. The functional node design module is used to design various types of functional nodes based on the built system, for constructing large model service processes; the functional nodes include input nodes, large model nodes, judgment nodes, URL nodes, knowledge base nodes, output nodes, and import pre-arranged process nodes. The process orchestration module is used to select, associate, and set attributes of functional nodes by dragging and dropping functional nodes in a graphical interface based on the designed functional nodes and the built system, so as to build a large model service process. The process management module is used for file management and version management of the large model service processes that are built; The process testing and optimization module is used to test and optimize the service processes of the large model that has been built. The data processing module is used to store process data of the large model service process using distributed caching and database technology, and to encrypt and transmit the process data.

[0012] Thirdly, embodiments of this application also provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the process management method for large model services as described in any of the above.

[0013] Fourthly, embodiments of this application also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the process management method for the large model service as described in any of the above.

[0014] As can be seen from the above technical solutions, the present invention has the following advantages: The process management method for large model services provided in this application builds a highly available and easily scalable system foundation through front-end and back-end separation, microservice architecture, and containerization technology. Combined with graphical drag-and-drop orchestration and diverse functional nodes (such as large model inference, condition judgment, knowledge base invocation, etc.), it realizes flexible customization and visual development of large model service processes. Through version management, automated testing optimization, and distributed storage encryption mechanisms, it ensures the traceability, security, and operational efficiency of the process, while supporting fault self-healing and dynamic resource scheduling, significantly improving the agility, collaboration efficiency, and business adaptability of large model service development and deployment.

[0015] This application integrates front-end / back-end separation, microservice architecture, and containerization technology to achieve modular deployment and dynamic scaling, supporting on-demand resource allocation and significantly improving service availability and fault tolerance. Microservice decomposition reduces the risk of single points of failure, containerized images ensure environmental consistency, and combined with Spring Cloud's service governance capabilities, it guarantees stable operation in complex scenarios.

[0016] This application features a variety of pre-built functional nodes covering the entire chain of requirements from input to output, allowing users to quickly build complex processes through graphical drag-and-drop functionality. The modular design supports node reuse and logic nesting, significantly shortening the development cycle. It also supports importing orchestrated processes, enabling cross-project experience accumulation and rapid iteration.

[0017] This application lowers the technical barrier with its visual interface based on Angular+G6, allowing users to design processes intuitively through drag-and-drop without coding. The parameter configuration interface clearly guides input and output settings. Process deserialization and version rollback functions further simplify debugging and maintenance, enabling non-technical personnel to efficiently manage large model services.

[0018] This application's file management system, combined with version control, enables full lifecycle tracking of the workflow and supports multi-user collaboration and branch development. Distributed database storage ensures data persistence, deletion protection mechanisms prevent accidental deletion, and the integration of simulated data testing and monitoring tools guarantees controllable workflow quality.

[0019] This application employs HTTPS encrypted transmission and distributed caching technology, combined with input validation and filtering, to comprehensively protect user data privacy and process log security. It leverages Kubernetes' automatic failover capabilities to ensure seamless service switching in the event of container failure, and works in conjunction with Spring Boot's circuit breaker and degradation strategies to build a highly reliable runtime environment, effectively resisting external attacks and internal anomalies. Attached Figure Description

[0020] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating the process management method for the large model service provided in this application.

[0022] Figure 2 A schematic diagram of the process management system for the large model service provided in this application.

[0023] Figure 3A schematic diagram of the structure of the electronic device provided in this application. Detailed Implementation

[0024] The various embodiments of this disclosure will be described more fully in the detailed steps of the process management method for large model services described below. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0025] In the following, the terms “comprising” or “may include”, which may be used in various embodiments of this disclosure, indicate the presence of the disclosed functions, operations, or elements, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in various embodiments of this disclosure, the terms “comprising,” “having,” and their cognates are intended only to indicate a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or the possibility of adding one or more combinations of the foregoing.

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0027] Please see Figure 1 The diagram shown is a flowchart of a process management method for a large model service in a specific embodiment. The method includes: S1: The system is built using a front-end / back-end separation architecture and a microservice architecture, combined with containerization technology.

[0028] In this specific implementation, a front-end and back-end separation system architecture is adopted. The Angular framework and g6 components are used on the front end to implement the interface display and process visualization, while the Spring Boot framework is used on the back end to handle data and manage processes. The large model module is broken down into microservices, and the Spring Cloud framework is used to implement the registration, discovery, and governance functions of the microservices. Finally, Kubernetes containerization technology is used to package the microservices into container images.

[0029] For example, a front-end / back-end separation architecture is adopted. The front-end is responsible for user interface display and interaction, while the back-end is responsible for data processing and process management. The front-end uses the modern Angular framework to enable rapid development and dynamic updates of the user interface, and the process page visualization is developed using g6 components. The back-end uses Java frameworks such as Spring Boot to implement data processing and process management logic. Simultaneously, different large model modules are broken down into microservices, each deployed and running independently. Microservice frameworks such as Spring Cloud are used to implement functions such as service registration and discovery, load balancing, circuit breaking, and degradation. Kubernetes containerization technology is used to package the microservices into container images for easy subsequent rapid deployment and migration.

[0030] S2: Based on the built system, design various types of functional nodes to construct large model service processes; the functional nodes include input nodes, large model nodes, judgment nodes, URL nodes, knowledge base nodes, output nodes, and import pre-arranged process nodes.

[0031] In its specific implementation, this method designs various types of functional nodes, including input nodes, large model nodes, decision nodes, URL nodes, knowledge base nodes, output nodes, and nodes for importing pre-arranged processes. Each node type has specific functions and roles, capable of meeting different business needs.

[0032] Input node: Used to receive information, data or instructions from the user, providing various formats such as text and attachments, serving as the starting point of the overall process.

[0033] Large Model Nodes: These nodes integrate and run large-scale industry models, intelligently processing user input, including inference and prediction, and generating responses and data results. Specifically, large model nodes utilize deep learning frameworks such as TensorFlow and PyTorch to integrate and run large-scale industry models. Users can upload custom large models to the system via configuration files or API interfaces, specifying model input and output parameters and running conditions through node configuration. The system loads and runs the large model based on the user configuration, intelligently processing user input and generating responses.

[0034] Decision nodes: These nodes evaluate user input or the output of the previous functional node based on preset conditions to determine the execution path of the subsequent process. Specifically, decision nodes evaluate user input or the output of the previous node based on preset conditions. These conditions can be implemented using various methods such as text matching, regular expressions, and numerical comparison. When the user input or the output of the previous functional node meets the conditions, the system executes the subsequent process according to the preset path; otherwise, it executes another path. This mechanism allows the process management module to flexibly handle various complex situations.

[0035] URL nodes are used to access and invoke external resources or services, such as web addresses, APIs, databases, etc., to obtain relevant information.

[0036] Knowledge base nodes: Used to store and manage knowledge base information for specific domains and industries, and can provide data-level support and information integration for large model nodes.

[0037] Output node: Used to output the final result or data, serving as the endpoint of the entire process. Output the processing results in a user-friendly manner, including text, voice, and images.

[0038] Import node: Allows users to import and copy pre-arranged large model processes for secondary editing or reuse, providing excellent reusability for complex large model processes.

[0039] S3: Based on the designed functional nodes and the built system, functional nodes can be selected, associated, and their attributes set by dragging and dropping in the graphical interface to build a large model service process.

[0040] In a specific implementation, based on the designed functional nodes and the built system, different functional node types are selected by dragging and dropping functional nodes in a graphical interface. These nodes are then connected in a specific order and logical relationship to construct a large-scale model service process that meets the requirements. During the process orchestration, users can set the attributes of each node, such as input parameters, output parameters, and calling conditions. Simultaneously, users can configure each node, including input parameters, output parameters, and running conditions, to meet specific needs. For example, the process first obtains the user's question through the input node, then determines the question type through the judgment node, selects the appropriate large-scale model node for processing based on the judgment result, and finally feeds back the processing result to the user through the output node, thus constructing a complete large-scale model service process.

[0041] S4: Perform file management and version management on the large model service process that is built.

[0042] In specific implementations, process management functions are provided, including saving, loading, modifying and deleting processes. Users can save and load the arranged processes at any time for reuse or modification. Process version management is supported, making it convenient for users to track and compare the differences between different versions.

[0043] As an example, the process management method used in this step is as follows: Workflow Saving: After the user completes the workflow arrangement, the system serializes the workflow's node information, connection relationships, configuration parameters, and other data to generate workflow files, which are then stored in a distributed database. Simultaneously, a unique identifier is generated for each workflow file to facilitate subsequent retrieval and management.

[0044] Process loading: Users can select a saved process file through the process list interface. The system performs deserialization based on the data in the file and restores the process to the arrangement interface. Users can view the detailed information of the process and make further edits or run it directly.

[0045] Process Modification: Users can add or delete nodes, adjust connections, and modify parameters in a loaded process. The system saves modification records in real time to ensure the latest state of the process is preserved. After modification, users can retest the process to verify whether the modified process meets business requirements.

[0046] Process Deletion: Users can select and delete process files that are no longer needed. The system will prompt for confirmation before deletion to prevent accidental deletion. The deletion operation will completely remove the process file and its related data from the distributed database.

[0047] Workflow Version Management: Whenever a user saves a workflow, the system automatically generates a version number for that workflow and records information such as the version's creation time and modifications. Users can view a list of different workflow versions in the workflow version management interface, compare the differences between versions, and select the appropriate version to restore or refer to.

[0048] S5: Perform process testing and optimization on the constructed large model service process.

[0049] In a specific implementation, after the user completes the custom orchestration of the process, a process testing function is provided, allowing the user to test and optimize the process before formal deployment. Through process testing, adjustments can be made based on the test results, such as modifying node parameters and optimizing node connection logic, to improve process efficiency and accuracy. Simultaneously, monitoring and analysis of process execution data are supported for continuous process improvement.

[0050] For example, process testing and optimization involves using tools such as Postman and JMeter to perform functional and performance tests on the process, and using monitoring tools such as Prometheus to monitor runtime data in real time. Based on the test results, node parameters and connection logic are adjusted to improve process efficiency and accuracy.

[0051] S6: Distributed caching and database technology are used to store the process data of the large model service process, and the process data is transmitted in encrypted form.

[0052] In specific implementations, distributed caching and database technologies are used to store user input and output data, process operation logs, and other information. Distributed caching stores temporary data to improve system response speed; distributed database stores persistent data to ensure data security and consistency, while providing data backup and recovery functions to prevent data loss or damage.

[0053] Data is transmitted using encrypted protocols such as HTTPS to ensure data transmission security. User input is validated and filtered to prevent malicious attacks and data leaks. Microservice architecture and containerized deployment are used to improve system reliability and fault tolerance. When a microservice or container fails, the system automatically detects and switches to a backup instance to continue running.

[0054] In this embodiment, the system is built using front-end and back-end separation, microservices, and containerization technologies. Multiple functional nodes are designed to construct a large model service process. The process construction is simplified through drag-and-drop operations in a graphical interface. File and version management facilitates process maintenance. Professional tools are used for process testing and optimization to ensure service quality. Distributed caching and database technologies are used, and encrypted transmission of process data ensures data security and system stability. Overall, the construction efficiency, maintainability, service quality, and data security of the large model service process are improved.

[0055] like Figure 2 As shown, the following are embodiments of the process management system for large model services provided in this disclosure. This system and the process management methods for large model services in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the process management system for large model services, please refer to the embodiments of the process management methods for large model services described above.

[0056] A process management system for large-scale model services includes: a technical architecture building module, a functional node design module, a process orchestration module, a process management module, a process testing and optimization module, and a data processing module.

[0057] The technical architecture building module is used to build systems using a front-end / back-end separation architecture and a microservice architecture, combined with containerization technology.

[0058] The functional node design module is used to design various types of functional nodes based on the built system, for constructing large model service processes; the functional nodes include input nodes, large model nodes, judgment nodes, URL nodes, knowledge base nodes, output nodes, and import pre-arranged process nodes.

[0059] The process orchestration module is used to select, associate, and set attributes of functional nodes by dragging and dropping them in a graphical interface, based on the designed functional nodes and the built system, in order to build a large model service process.

[0060] The process management module is used for file management and version management of the large model service processes that are built.

[0061] The process testing and optimization module is used to test and optimize the large model service processes that have been built.

[0062] The data processing module is used to store process data of the large model service process using distributed caching and database technology, and to encrypt and transmit the process data.

[0063] The large model service process management system provided in this embodiment integrates front-end and back-end separation, microservice architecture, and containerization technology to build the system foundation. With the help of diverse functional nodes and intuitive graphical drag-and-drop operations, it makes the construction of large model service processes more convenient and efficient. At the same time, a comprehensive file and version management mechanism ensures the traceability and maintainability of the process; professional process testing and optimization methods effectively improve service performance; distributed caching and database technology, combined with encrypted transmission, safeguard data security and system stability, bringing significant benefits to the comprehensive management of large model service processes.

[0064] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0065] The process management method for large model services provided in this application can be applied to electronic devices. Those skilled in the art will understand that the electronic device structure involved in the embodiments of this invention does not constitute a limitation on the electronic device. An electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements. In the embodiments of this invention, the electronic device includes, but is not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments of this application described and / or claimed herein.

[0066] Electronic devices may include processors, external memory interfaces, internal memory, universal serial bus (USB) interfaces, charging management modules, power management modules, batteries, wireless communication modules, audio modules, speakers, microphones, sensor modules, buttons, cameras, displays, and SIM card interfaces, etc.

[0067] A processor may include one or more processing units, such as: a central processing unit (CPU), an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors.

[0068] The processor can serve as the nerve center and command center of an electronic device. The controller can generate operation control signals based on the instruction opcode and timing signals to control the fetching and execution of instructions.

[0069] The processor may also include memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory. This memory can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can retrieve it directly from this memory. This avoids repeated accesses, reduces processor latency, and thus improves system efficiency.

[0070] An external storage interface (ESI) can be used to connect external memory cards, such as microSD cards, to expand the storage capacity of electronic devices. The external memory card communicates with the processor through the ESI to perform data storage functions, such as saving music and video files on the external memory card.

[0071] Internal memory can be used to store computer executable program code, which includes instructions. The processor executes various functional applications and data processing of electronic devices by running the instructions stored in internal memory. Internal memory can include a program storage area and a data storage area. Internal memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0072] Wireless communication functionality in electronic devices can be achieved through antennas, wireless communication modules, modem processors, and baseband processors.

[0073] Wireless communication modules can provide solutions for wireless communication applications in electronic devices, including wireless local area networks (WLANs) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies.

[0074] Electronic devices can implement audio functions through audio modules, speakers, receivers, microphones, headphone jacks, and application processors.

[0075] Electronic devices can achieve shooting functions through ISPs, cameras, video codecs, GPUs, displays, and application processors.

[0076] Electronic devices can achieve display functions through GPUs, displays, and application processors.

[0077] A GPU is a microprocessor for image processing, connected to the display screen and application processor. GPUs are used to perform mathematical and geometric calculations for graphics rendering. A processor may include one or more GPUs, which execute program instructions to generate or modify display information.

[0078] A display screen is used to display images, videos, etc. A display screen includes a display panel.

[0079] The aforementioned electronic device implements the process management method for the large model service of this application. By adopting a front-end and back-end separation architecture, a microservice architecture combined with containerization technology to build the system, designing multiple functional nodes and using a graphical interface drag-and-drop operation to build the large model service process, implementing file and version management, process testing and optimization, and using distributed caching and database technology to encrypt the transmission of process data, it achieves the beneficial effects of improving process construction efficiency, enhancing process maintainability, optimizing service performance, ensuring data security and stable system operation.

[0080] The storage medium provided in this application stores a program product that enables a process management method for large-scale model services.

[0081] The process management methods for large model services include: The system is built using a front-end / back-end separation architecture and a microservice architecture, combined with containerization technology. Based on the system, various types of functional nodes are designed to construct large model service processes; the functional nodes include input nodes, large model nodes, judgment nodes, URL nodes, knowledge base nodes, output nodes, and nodes for importing orchestrated processes. Based on the designed functional nodes and the built system, functional nodes can be selected, associated, and their attributes set by dragging and dropping in the graphical interface to build a large model service process. Perform file and version management on the large model service process that is built; Perform process testing and optimization on the constructed large-scale model service process; Distributed caching and database technology are used to store process data of large model service processes, and the process data is transmitted in encrypted form. In some possible implementations, the process management method for the large model service of this disclosure can be implemented as a program product that includes program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of this disclosure.

[0082] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0083] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A process management method for large-scale model services, characterized in that, include: The system is built using a front-end / back-end separation architecture and a microservice architecture, combined with containerization technology. Based on the system built, various types of functional nodes are designed to construct large-scale model service processes; The functional nodes include input nodes, large model nodes, judgment nodes, URL nodes, knowledge base nodes, output nodes, and import pre-arranged process nodes. Based on the designed functional nodes and the built system, functional nodes can be selected, associated, and their attributes set by dragging and dropping in the graphical interface to build a large model service process. Perform file and version management on the large model service process that is built; Perform process testing and optimization on the constructed large-scale model service process; Distributed caching and database technology are used to store process data of large model service processes, and the process data is transmitted in encrypted form.

2. The process management method for large model services according to claim 1, characterized in that, The system is built using a front-end / back-end separation architecture and a microservice architecture, combined with containerization technology, including: The system adopts a front-end and back-end separation architecture. The front-end uses the Angular framework and g6 components to implement interface display and process visualization, while the back-end uses the Spring Boot framework to process data and manage processes. The large model module is broken down into microservices. The Spring Cloud framework is used to implement the registration, discovery and governance functions of the microservices, and the microservices are packaged into container images using Kubernetes containerization technology.

3. The process management method for large model services according to claim 2, characterized in that: The input node is used to receive information, data or instructions input by the user, generate text or attachments, and serve as the starting point of the process. The large model node is used to integrate and run large industry models in the domain, perform reasoning and prediction on user input, and generate answers and data results based on the results. The judgment node is used to judge the user input or the output of the previous functional node according to preset conditions, and determine the execution path of the subsequent process. The URL node is used to access and invoke external resources or services and obtain relevant information; The knowledge base node is used to store and manage knowledge base information for specific domains and industries, and to provide data support and information integration for the large model node. The output node is used to generate the processing result and output the processing result in the form of text, voice or image, as the end point of the entire process; The "Import Arranged Process Nodes" is used to import and copy pre-arranged large model processes.

4. The process management method for large model services according to claim 3, characterized in that, The system, based on the designed functional nodes and the constructed system, allows users to select, associate, and set attributes of functional nodes by dragging and dropping them in a graphical interface, thereby building a large model service process, including: Based on the designed functional nodes and the built system, different functional node types can be selected by dragging and dropping functional nodes in the graphical interface, and connected in a specific order and logical relationship to build a large model service process that meets the requirements. Configure the input parameters, output parameters, calling conditions, and running conditions for each functional node.

5. The process management method for large model services according to claim 4, characterized in that, The process of managing files and versions for the constructed large model service includes: After completing the construction of the large model service process, the functional node information, connection relationship and attribute parameters of the process are serialized to generate process files, which are then stored in a distributed database, and a unique identifier is generated for each process file. Select a saved process file through the process list interface, perform a deserialization operation on the process file, restore the process to the arrangement interface, and then edit or load the process. Perform node addition / deletion, connection relationship adjustment, or parameter modification operations on the loaded large model service process, and save the modification record in real time; Select the process files that are no longer needed and delete them to completely remove the process files and their related data from the distributed database; When a process file is saved, a version number is automatically generated for that process, and the creation time and modifications of that process file version are recorded.

6. The process management method for large model services according to claim 5, characterized in that, The process testing and optimization of the constructed large model service process includes: Create simulation data, perform functional and performance tests on the large model service process using Postman and JMeter tools, and use the monitoring tool Prometheus to monitor the running data in real time; Adjust the functional node parameters and connection logic based on the test results.

7. The process management method for large model services according to claim 6, characterized in that, The process data of the large model service flow is stored using distributed caching and database technology, and the process data is transmitted in encrypted form, including: Distributed caching and database technologies are used to store user input and output data as well as process execution logs; User input is validated and filtered, and data is transmitted using the HTTPS encryption protocol; When a microservice or container fails, it is automatically detected and switched to a standby instance to continue running.

8. A process management system for large-scale model services, characterized in that, The system employs the process management method for large model services as described in any one of claims 1 to 7; The system includes: The technical architecture building module is used to build systems using a front-end / back-end separation architecture and a microservice architecture, combined with containerization technology. The functional node design module is used to design various types of functional nodes based on the built system, for constructing large model service processes; the functional nodes include input nodes, large model nodes, judgment nodes, URL nodes, knowledge base nodes, output nodes, and import pre-arranged process nodes. The process orchestration module is used to select, associate, and set attributes of functional nodes by dragging and dropping functional nodes in a graphical interface based on the designed functional nodes and the built system, so as to build a large model service process. The process management module is used for file management and version management of the large model service processes that are built; The process testing and optimization module is used to test and optimize the service processes of the large model that has been built. The data processing module is used to store process data of the large model service process using distributed caching and database technology, and to encrypt and transmit the process data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the process management method for large model services as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the process management method for large model services as described in any one of claims 1 to 7.

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