Application access method, apparatus, device, medium and program product

Abstract

This application provides an application access method, apparatus, device, medium, and program product, which can be applied to the field of artificial intelligence, specifically involving the application of large models in application access scenarios. The method includes: semantic parsing of access requirement data based on a large model; retrieving the semantic parsing results based on a knowledge graph and the large model to generate an application access scheme; wherein the knowledge graph is constructed based on historical access data; executing application access tasks in the application access scheme according to the knowledge graph, and monitoring the execution status of the application access tasks in real time; and verifying the execution results of the application access tasks based on a verification scheme, adjusting the application access tasks and executing the adjusted application access tasks if the verification fails, until the verification passes; wherein the verification scheme is generated based on scenario analysis of the semantic parsing results using the knowledge graph.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence, specifically to the application of large models in application access scenarios, and more specifically to an application access method, device, equipment, medium, and program product. Background Technology

[0002] In the information and digital age, especially in complex business scenarios such as fintech, application integration is often used to connect, interact, or integrate an application (or system) with other applications (or systems) to achieve data sharing, functional collaboration, or business linkage. In fintech system development, when adding new functions requires integration with other applications, a series of complex preparatory work is often required. For example, application integration may involve key nodes such as core trading systems, risk control engines, third-party payment interfaces, and credit data platforms, and each step must strictly adhere to financial regulatory requirements to ensure compliant and encrypted data transmission and precise access control.

[0003] Traditional application integration processes rely on manual operations, such as manually consulting documentation to define firewall rules, offline application for server resources, registration of system call relationships, development of verification plans, and item-by-item production checks. These manual processes are cumbersome, time-consuming, labor-intensive, inefficient, and prone to errors. Therefore, as enterprises increasingly increase their level of informatization, traditional methods are no longer sufficient to meet system integration needs. Summary of the Invention

[0004] In view of the above problems, this application provides automated application access methods, apparatus, devices, media and program products.

[0005] According to a first aspect of this application, an application access method is provided, the method comprising: performing semantic parsing on access requirement data based on a large model; retrieving the semantic parsing results based on a knowledge graph and the large model to generate an application access scheme; wherein the knowledge graph is constructed based on historical access data; executing application access tasks in the application access scheme according to the knowledge graph and monitoring the execution status of the application access tasks in real time; and verifying the execution results of the application access tasks based on a verification scheme, and if the verification fails, adjusting the application access tasks and executing the adjusted application access tasks until the verification passes; wherein the verification scheme is generated based on scenario analysis of the semantic parsing results using the knowledge graph.

[0006] According to an embodiment of this application, executing the application access task in the application access scheme based on the knowledge graph includes: establishing a communication connection with the resource management system through an application programming interface (API) based on the application access task in the application access scheme; obtaining a form template from the knowledge graph that matches the resource application type in the application access task, wherein the form template includes a mapping relationship between form fields and entities; parsing the resource parameters in the application access task; matching the resource parameters with the form fields according to the entity mapping relationship; filling the matched resource parameters into the corresponding form fields based on the form template to generate a resource application form; and submitting the resource application form to the resource management system through the API.

[0007] According to an embodiment of this application, the step of executing the application access task in the application access scheme based on the knowledge graph further includes: establishing a communication connection with the firewall management system through the application programming interface based on the application access task in the application access scheme; obtaining a firewall rule template matching the business scenario in the application access task from the knowledge graph; and submitting an activation application to the firewall management system through the application programming interface based on the firewall rule template.

[0008] According to an embodiment of this application, the semantic parsing of access requirement data based on a large model includes: parsing the access requirement data based on the large model to extract entity data and key access information; and mapping the entities in the entity data to entities in the knowledge graph.

[0009] According to an embodiment of this application, the semantic parsing result includes the entity data and the access key information; the step of retrieving the semantic parsing result based on the knowledge graph and the large model to generate an application access scheme includes: performing case retrieval on the entity data in the knowledge graph, identifying similar historical cases, and extracting the process elements of the similar historical cases; generating an initial access process by reasoning about the process elements through the large model based on the access rules of the knowledge graph and the access key information; and optimizing the initial access process based on the resource configuration status to generate the application access scheme.

[0010] According to an embodiment of this application, the method further includes: collecting call relationship information; wherein the call relationship information includes the caller, the callee, interface parameters, and call frequency; and generating a call relationship graph based on the knowledge graph according to the call relationship information.

[0011] According to an embodiment of this application, the generation process of the verification scheme includes: matching the access key information with scene classification entities in the knowledge graph to determine similar scene categories, and determining a verification template based on the similar scene categories; and adjusting the verification template through the large model according to the access elements of the access key information to generate the verification scheme.

[0012] According to an embodiment of this application, the method further includes: if verification is passed, checking the preconditions for the application access task by traversing the dependencies of the knowledge graph; and identifying the risk points of the application access task, and providing risk warnings based on the risk points.

[0013] According to an embodiment of this application, the real-time monitoring of the execution status of the application access task includes: real-time monitoring of the execution status of the application access task, wherein the execution status includes execution progress and execution status; visualizing the execution progress corresponding to the application access task; and, in the event of an abnormality in the execution status corresponding to the application access task, triggering an exception handling mechanism and adjusting the application access task through the exception handling mechanism.

[0014] According to an embodiment of this application, the knowledge graph construction process includes: constructing an entity relationship graph based on a triplet structure; and performing natural language processing on the historical access data to extract access entities and access relationships, and injecting the access entities and access relationships into the entity relationship graph to construct the knowledge graph.

[0015] A second aspect of this application provides an application access device, comprising: a semantic parsing module for performing semantic parsing on access requirement data based on a large model; a scheme generation module for retrieving the semantic parsing results based on a knowledge graph and the large model to generate an application access scheme; wherein the knowledge graph is constructed based on historical access data; an access task execution module for executing application access tasks in the application access scheme according to the knowledge graph and monitoring the execution status of the application access tasks in real time; and an access task verification module for verifying the execution results of the application access tasks based on a verification scheme, and adjusting the application access tasks and executing the adjusted application access tasks if the verification fails, until the verification passes; wherein the verification scheme is generated based on scenario analysis of the semantic parsing results using the knowledge graph.

[0016] A third aspect of this application provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0017] A fourth aspect of this application also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0018] The fifth aspect of this application also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0019] In the embodiments of this application, access requirements are automatically parsed through a large model, and a standardized application access scheme is generated by combining a knowledge graph. The application access tasks in the application access scheme are executed based on the knowledge graph, thereby realizing the automated processing of application access. By monitoring and verifying the application access tasks in real time, the entire process of application access preparation is automated and intelligent, improving access efficiency, reducing manual operation costs, and reducing access errors. Attached Figure Description

[0020] The above-mentioned contents, other objects, features and advantages of this application will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0021] Figure 1 The illustrations depict application scenarios of the application access method, apparatus, device, medium, and program product according to embodiments of this application.

[0022] Figure 2 A flowchart illustrating an application access method according to an embodiment of this application is shown schematically.

[0023] Figure 3 This illustration schematically shows a flowchart of the knowledge graph construction process for the application access method according to an embodiment of this application;

[0024] Figure 4 This illustration schematically shows a flowchart of the application access method for generating an access scheme according to an embodiment of this application;

[0025] Figure 5 This illustration schematically shows a real-time monitoring flowchart of an application access method according to an embodiment of this application;

[0026] Figure 6 This illustration schematically shows another flowchart of an application access method according to an embodiment of this application;

[0027] Figure 7 This illustration schematically shows an automated processing flowchart of an application access method according to an embodiment of this application;

[0028] Figure 8 This schematically illustrates a structural block diagram of an application access device according to an embodiment of this application; and

[0029] Figure 9 A block diagram schematically illustrates an electronic device suitable for implementing an application access method according to an embodiment of this application. Detailed Implementation

[0030] The embodiments of this application will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of this application. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of this application for ease of explanation. However, it will be apparent that one or more embodiments may be implemented without these specific details. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of this application.

[0031] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of this application. The terms “comprising,” “including,” etc., as used herein indicate the presence of features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0032] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0033] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0034] For existing systems, the functionality of a single application is often limited. Therefore, integrating other applications can quickly fill gaps in the system's capabilities. The data accumulated by different applications is complementary, and data synergy can be achieved after integration. Currently, the traditional approach to application integration mainly relies on manual operations, such as manually reviewing documentation to determine firewall access rules, applying for server resources through offline processes, manually registering inter-system call relationships, manually developing verification plans, and conducting pre-deployment checks item by item.

[0035] This manual processing method has many drawbacks: First, the operation process is cumbersome and time-consuming, requiring a large investment of manpower. However, fintech businesses have extremely high requirements for timeliness, and a lengthy process may cause missed market opportunities. Second, the financial industry has stringent requirements for data security and system stability. The subjectivity and uncertainty of human operation can easily lead to information omissions or configuration errors, which in turn can cause security risks or business interruptions. In addition, different financial applications (such as payment systems, risk control platforms, credit reporting systems, etc.) have different access standards and compliance requirements. Manual processing cannot guarantee the consistency and standardization of the process, nor can it achieve real-time monitoring and compliance auditing of the entire access process.

[0036] This application provides an application access method, comprising: semantic parsing of access requirement data based on a large model; retrieving the semantic parsing results based on a knowledge graph and the large model to generate an application access scheme; wherein the knowledge graph is constructed based on historical access data; executing application access tasks in the application access scheme according to the knowledge graph and monitoring the execution status of the application access tasks in real time; and verifying the execution results of the application access tasks based on a verification scheme, adjusting the application access tasks and executing the adjusted application access tasks if the verification fails, until the verification passes; wherein the verification scheme is generated based on scenario analysis of the semantic parsing results using the knowledge graph. In this application's embodiments, access requirements are automatically parsed using a large model, and a standardized application access scheme is generated by combining it with a knowledge graph. Application access tasks in the application access scheme are executed based on the knowledge graph, achieving automated processing of application access. By monitoring and verifying application access tasks in real time, the entire process of application access preparation is automated and intelligent, improving access efficiency, reducing manual operation costs, and minimizing access errors.

[0037] Figure 1 The illustration shows an application scenario diagram of the application access method, apparatus, device, medium, and program product according to embodiments of this application.

[0038] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0039] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0040] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0041] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0042] It should be noted that the application access method provided in this application embodiment can generally be executed by server 105. Correspondingly, the application access device provided in this application embodiment can generally be located in server 105. The application access method provided in this application embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the application access device provided in this application embodiment can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0043] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0044] The following will be based on Figure 1 The described scene, through Figures 2-7 The application access method according to the embodiments of this application will be described in detail.

[0045] Figure 2 A flowchart illustrating an application access method according to an embodiment of this application is shown schematically.

[0046] like Figure 2 As shown, the application access method in this embodiment includes operations S210 to S240. This application access method is not limited to a specific execution subject. The execution subject can be any electronic device, such as a terminal device or a server device, etc. The execution subject can also be any software application or client.

[0047] When operating S210, semantic parsing is performed on the access requirement data based on the large model.

[0048] Users can input access requirement data (such as access scenario description, core business information, related business systems, access methods, interface information, performance requirements, etc.) through the front-end page. Because user-input access requirement data is often highly flexible, it may contain colloquial expressions, implicit information, or cross-domain terminology, rather than content with a fixed format or high structure, making it difficult for traditional models to parse. Due to the deep understanding and generalization capabilities of large-scale models for complex, unstructured natural language requirements, using large-scale models to parse access requirement data can accurately interpret user access needs. Large-scale models are irreplaceable in semantic parsing of requirements.

[0049] Large-scale pre-trained language models are artificial intelligence models trained on ultra-large-scale data and possessing massive parameters. They achieve semantic understanding and generation of natural language through deep learning technology. By semantically parsing the access requirements data through large-scale models, unstructured access requirements (such as natural language, colloquial expressions, etc.) are transformed into structured, understandable information (such as entity data and key access information).

[0050] When operating S220, based on knowledge graphs and large models, semantic parsing results are retrieved to generate application access solutions; among them, knowledge graphs are constructed based on historical access data.

[0051] A structured knowledge graph is pre-built based on historical access data. The knowledge graph integrates various types of knowledge related to application access, including firewall configuration rules, server resource types, system call relationship models, verification scheme templates, and pre-deployment checklists.

[0052] By using a large model to perform semantic parsing on the user's input access requirements data, and then combining historical access knowledge and resource information from the knowledge graph, a personalized application access solution is generated.

[0053] When operating S230, the application access task in the application access scheme is executed according to the knowledge graph, and the execution status of the application access task is monitored in real time.

[0054] Based on the application access scheme, the system clearly defines the application access tasks and automatically completes various access preparation tasks such as firewall activation, server application, and call relationship registration. It monitors the progress of the entire access preparation process in real time, tracking the processing status and recording the results of each stage of the application access task.

[0055] In operation S240, the execution result of the application access task is verified based on the verification scheme. If the verification fails, the application access task is adjusted and executed until the verification is passed. The verification scheme is generated by performing scenario analysis on the semantic parsing results based on the knowledge graph.

[0056] After automating the application integration task, verification is performed based on the execution results and a pre-generated verification plan to ensure that all preparations meet the requirements. If verification fails, the application integration task needs to be adjusted, such as modifying the interface type in the task to ensure interface compatibility. After modification, the application integration task is executed again until verification passes. If verification passes, production testing can proceed.

[0057] In the embodiments of this application, access requirements are automatically parsed through a large model, and a standardized application access scheme is generated by combining a knowledge graph. The application access tasks in the application access scheme are executed based on the knowledge graph, thereby realizing the automated processing of application access. By monitoring and verifying the application access tasks in real time, the entire process of application access preparation is automated and intelligent, improving access efficiency, reducing manual operation costs, and reducing access errors.

[0058] According to an embodiment of this application, the process of generating a verification scheme includes: matching access key information with scene classification entities in a knowledge graph to determine similar scene categories, and determining a verification template based on similar scene categories; and adjusting the verification template using a large model based on the access elements of the access key information to generate a verification scheme.

[0059] The generation of a verification scheme first requires identifying the access scenario. By combining key access information with scenario classification entities in the knowledge graph (such as data interaction scenarios, function integration scenarios, performance expansion scenarios, etc.), the most similar scenario categories are matched to obtain similar scenario categories. Then, the large model can retrieve the corresponding verification template from the knowledge graph based on the similar scenario categories. The verification template is then adjusted according to the specific elements of the current access (such as interface type, data transmission volume, performance requirements, etc.) to generate a verification scheme that includes test steps, specific procedures, test objects, and expected results. For example, for the access of a data interaction interface, verification steps such as interface compatibility testing and data consistency testing are automatically generated, and test cases and expected results are clearly defined.

[0060] In the embodiments of this application, accurate verification schemes are quickly generated based on historical experience in the knowledge graph through intelligent matching and dynamic adjustment, thereby improving adaptability and efficiency, reducing labor costs, and ensuring the security and compliance of application access tasks.

[0061] Figure 3 A flowchart illustrating the knowledge graph construction process of the application access method according to an embodiment of this application is shown.

[0062] like Figure 3 As shown, according to an embodiment of this application, the knowledge graph construction process includes operations S310 to S320.

[0063] When operating S310, an entity relationship graph is constructed based on the triplet structure.

[0064] An entity relationship graph is constructed using a triplet structure (entity-relationship-entity). Entities include: firewall rules, server types, application interfaces, call relationships, verification steps, etc. Each entity contains specific attributes; for example, the attributes of the firewall rule entity include port range, protocol type, and scope of application, while the attributes of the server type entity include the number of CPU cores, memory size, storage capacity, and supported operating systems. Relationships include: dependent on, belonging to, and requiring configuration. For example, a "requires configuration" relationship is established between the server request entity and the firewall rule entity.

[0065] When operating the S320, historical access data is processed by natural language to extract access entities and access relationships, and these entities and relationships are injected into the entity relationship graph to construct a knowledge graph.

[0066] The system extracts access entities and relationships from historical access data (such as historical access documents, online knowledge bases, and historical project records) using natural language processing technology, and adds them to the knowledge graph to achieve knowledge extraction and fusion. The system periodically accesses resource change information (such as firewall rule updates) and transforms this dynamic information into updated entities or relationships using preset extraction rules. Simultaneously, it performs conflict detection and fusion on knowledge from different sources to ensure the consistency and accuracy of the knowledge.

[0067] In addition, graph databases can be used to store knowledge graphs, supporting complex queries based on graph structures, such as querying all the dependent resources and configuration steps required for an application to connect.

[0068] In the embodiments of this application, the entity relationship graph of the triple structure is combined with natural language processing to construct a knowledge graph, realize the structured storage and reuse of historical access experience, and new access projects can directly refer to historical successful access cases to realize knowledge accumulation and reuse, and reduce repetitive work.

[0069] According to an embodiment of this application, the operation S210, which performs semantic parsing of access requirement data based on a large model, includes: parsing the access requirement data based on the large model, extracting entity data and key access information; and mapping the entities in the entity data to entities in the knowledge graph.

[0070] Based on a large model, semantic parsing is performed on the user-input access request text to extract entity data (such as target application, source application, server type, etc.) and key access information (such as source application information, target application information, functional modules, performance requirements, etc.), and mapped to corresponding entities in the knowledge graph. For example, if the user inputs that the payment module (source application) needs to access the financial system (target application), and the payment module is a functional module of the financial system, providing payment transaction data to the financial system, and the financial system is responsible for receiving and processing the data from the payment module, the large model can extract the payment module and the financial system as entities and identify the need to apply for a data interaction interface. Here, the payment module, as the source application (i.e., the party being accessed), provides specific services or data interfaces. The financial system, as the target application (i.e., the main application or core platform), is responsible for receiving and integrating external functions, actively initiating access actions, and needs to call or interface with other systems.

[0071] In the embodiments of this application, entity data is extracted from the requirement document by parsing the large model and mapped to the knowledge graph, thereby realizing the structured integration of information, improving data relevance and retrieval efficiency, enhancing knowledge reasoning ability, and improving the accuracy and efficiency of access requirement processing.

[0072] Figure 4 The flowchart illustrating the application access method for generating an access scheme according to an embodiment of this application is shown in the illustration.

[0073] like Figure 4 As shown, according to an embodiment of this application, the semantic parsing result includes entity data and access key information; in operation S220, based on knowledge graphs and large models, the semantic parsing result is retrieved to generate an application access scheme, which includes operations S410 to S430.

[0074] In operation S410, case retrieval is performed on entity data in the knowledge graph to identify similar historical cases and extract process elements from similar historical cases.

[0075] Based on the parsed entity data, historical access cases with similar entities and relationships to the current entity data are retrieved from the knowledge graph. Similar historical cases are obtained, and process elements are extracted from these similar historical cases. Process elements include the execution order of each step, resource dependencies, and constraints.

[0076] When operating S420, based on the access rules and key access information of the knowledge graph, the initial access process is generated by reasoning about the process elements through the large model.

[0077] The large model combines the access rules stored in the knowledge graph (such as the dependency relationship that the server application must be completed before the firewall configuration) with the uniqueness of the current requirements (such as special performance requirements) to infer and generate the initial access process.

[0078] When operating the S430, the initial access process is optimized based on the resource configuration status to generate an application access solution.

[0079] Based on real-time resource configuration status (such as server availability and firewall configuration margin), the initial access process is optimized to generate application access solutions. A large-scale model can be used to optimize the initial access process; for example, when certain server resources are scarce, the large-scale model can recommend alternative solutions or adjust application priorities to generate an access solution. The access solution includes application access tasks, which contain specific access processes. These processes clearly define the sequence and dependencies of steps such as firewall activation, server application, interface integration, and verification testing, ensuring that each step is logically sound and conforms to practical operational standards.

[0080] In the embodiments of this application, the reasoning and parsing capabilities of large-scale models are utilized to provide intelligent decision support. By combining knowledge graphs and large-scale models for access process reasoning, personalized initial access processes can be provided according to different access scenarios, automatically optimizing processes and resource allocation, providing accurate application access solutions, and improving the scientific nature of decision-making. Furthermore, through the accumulation of knowledge graphs and the learning capabilities of large-scale models, the access process can be continuously optimized, providing accurate and efficient solutions for application access in different scenarios.

[0081] According to an embodiment of this application, operation S230, which executes the application access task in the application access scheme based on the knowledge graph, includes: establishing a communication connection with the resource management system through an application programming interface (API) based on the application access task in the application access scheme; obtaining a form template from the knowledge graph that matches the resource application type in the application access task, wherein the form template includes a mapping relationship between form fields and entities; parsing the resource parameters in the application access task; matching the resource parameters with the form fields according to the entity mapping relationship; filling the matched resource parameters into the corresponding form fields based on the form template to generate a resource application form; and submitting the resource application form to the resource management system through the API.

[0082] By integrating with the resource management system through an Application Programming Interface (API), the resource application process is automated, enabling the automatic filling and submission of resource application forms. The submission process is as follows: First, based on various application form templates stored in the knowledge graph, these templates clearly define the mapping relationship between form fields and knowledge graph entities. Second, resource parameters (such as CPU core count, memory capacity, and storage size) in the application integration solution are parsed, and the resource parameters are accurately filled into the corresponding form positions through field matching. For dynamic information such as the applicant and application time, it is automatically retrieved from the system environment and populated. Finally, after completion, the form is automatically submitted to the approval process.

[0083] In the embodiments of this application, when executing application access tasks, the automated process and knowledge graph form template constraints ensure that the access preparation work of different projects follows unified standards and specifications, achieving standardization and normalization, and facilitating project management and subsequent maintenance. By matching fields in the form template, the form is automatically filled in, avoiding errors in filling in resource parameters such as servers and improving the accuracy of the access task. Resource requests are submitted to the resource management system through the application programming interface, realizing the automated application and configuration of resources such as servers.

[0084] According to an embodiment of this application, the operation S230 of executing the application access task in the application access scheme based on the knowledge graph further includes: establishing a communication connection with the firewall management system through an application programming interface based on the application access task in the application access scheme; obtaining a firewall rule template matching the business scenario in the application access task from the knowledge graph; and submitting an activation application to the firewall management system through the application programming interface based on the firewall rule template.

[0085] By integrating with the firewall management system via API, and based on firewall rule templates recorded in the knowledge graph, the system generates corresponding firewall access requests, automating the firewall access application process. The application process is as follows: First, it retrieves firewall rule templates from the knowledge graph that match the business scenario in the application access task. It then parses the firewall rule templates, extracting rule parameters (such as source internet address, destination internet address, port, and protocol type). Next, based on current business requirements, it instantiates the parameter fields in the firewall rule template, automatically generating rule configuration statements that meet the requirements of the target firewall system. Finally, it submits the access request to the firewall management system via API.

[0086] In the embodiments of this application, by automating the access to the firewall management system and submitting the activation application, the omissions and errors in manual operation, such as incorrect firewall rule configuration, are reduced, thereby improving the accuracy and reliability of the access work.

[0087] According to an embodiment of this application, when performing the application access task in the application access scheme, the application access method further includes registering the call relationship. The process of registering the call relationship includes: collecting call relationship information; wherein, the call relationship information includes the caller, the callee, interface parameters and call frequency; and generating a call relationship graph based on a knowledge graph according to the call relationship information.

[0088] An automatic call relationship registration mechanism is established in the system. Once the interface call relationship between a new function and other applications is determined, the call relationship information, such as the caller, the callee, interface parameters, and call frequency, is automatically entered into the knowledge graph to form a visualized call relationship graph.

[0089] For example, the process of generating the call relationship graph is as follows: First, the interface call requests between the newly added system function application and other applications are captured in real time, and the caller identifier, callee identifier, interface parameters, and call timestamp are extracted. Second, the number of calls to each interface within a preset time window is counted to generate call frequency data. Finally, the caller identifier, callee identifier, interface parameters, and call frequency data are used as associated node and edge attributes to generate an interactive call relationship graph based on the knowledge graph. The nodes in the call relationship graph represent the caller or callee, and the edges are labeled with interface parameters and call frequency.

[0090] In the embodiments of this application, call relationship information is collected; wherein, the call relationship information includes the caller, the callee, interface parameters and call frequency; and a call relationship graph is generated based on the knowledge graph according to the call relationship information.

[0091] Figure 5 A schematic diagram illustrating the real-time monitoring flowchart of the application access method according to an embodiment of this application is shown.

[0092] like Figure 5 As shown, according to an embodiment of this application, the real-time monitoring of the application access task execution in operation S230 includes operations S510 to S530.

[0093] When operating the S510, the execution status of application access tasks is monitored in real time, including execution progress and execution status.

[0094] When operating the S520, visualize the execution progress of the application access task.

[0095] Monitor the execution progress and status of each automated application access task in real time, and display the entire access preparation process in a visual way, including completed tasks, tasks being executed, and tasks that have not yet started.

[0096] When operating S530, if there is an abnormality in the execution status of the application access task, the exception handling mechanism is triggered to adjust the application access task.

[0097] When an exception occurs during the execution of a task, such as a failed firewall configuration API call or a rejected server request, the system will automatically record the exception information and trigger the exception handling mechanism. Based on the exception type and solutions in the knowledge graph, the exception handling mechanism attempts to resolve the exception automatically, such as by re-calling the API or adjusting server request parameters. If it cannot be resolved automatically, an exception notification is sent to relevant personnel, providing details of the exception and suggested solutions for manual intervention.

[0098] If no exceptions occur during task execution, no exception handling is required, and the task execution results can be verified subsequently.

[0099] In the embodiments of this application, by real-time monitoring and visualization of the execution progress and status of application access tasks, and by automatically triggering a processing mechanism in case of anomalies, the efficiency of task management and the stability of access tasks are significantly improved, the cost of manual intervention is reduced, and the entire process is automated for monitoring and processing. The access preparation work that traditionally takes several days to complete is shortened to several hours, greatly improving development efficiency and access efficiency.

[0100] Figure 6 Another flowchart illustrating an application access method according to an embodiment of this application is shown schematically.

[0101] like Figure 6 As shown, the application access method in this embodiment includes operations S610 to S670.

[0102] In operation S610, semantic parsing is performed on the access requirement data based on the large model. It should be noted that the method for operating S610 is the same as that for operating S210 described above, and will not be repeated here.

[0103] In operation S620, based on the knowledge graph and large model, the semantic parsing results are retrieved to generate an application access scheme; the knowledge graph is constructed based on historical access data. It should be noted that the method for operating S620 is the same as that for operating S220 described earlier, and will not be repeated here.

[0104] When operating S630, the application access tasks in the application access scheme are executed based on the knowledge graph, and the execution status of the application access tasks is monitored in real time. It should be noted that the method of operating S630 is the same as that of operating S230 described above, and will not be repeated here.

[0105] In operation S640, the execution result of the application access task is verified based on the verification scheme. It should be noted that the verification method of operation S640 is the same as that of operation S240 described above, and will not be repeated here.

[0106] In operation S650, if verification fails, the application access task is adjusted and executed until verification is successful. The verification scheme is generated based on scenario analysis of the semantic parsing results using a knowledge graph. It should be noted that the adjustment method for operation S650 is the same as that described in operation S240 above, and will not be repeated here.

[0107] When operating the S660, if the verification is successful, the system traverses the dependencies of the knowledge graph to check the preconditions for application access tasks, identifies the risk points of application access tasks, and provides risk warnings based on the risk points.

[0108] After successful verification, further production testing is required, including dependency checks and risk warnings. Dependency checks can be performed by traversing the dependencies in the knowledge graph to verify whether all prerequisites for access preparation have been met. For example, this includes checking whether the server has been assigned an Internet Protocol address, whether firewall rules are in effect, and whether the API documentation has been updated.

[0109] Risk warnings can leverage large-scale model analysis to identify risk points in historical integration cases, combine this with the current integration scenario, identify potential risks in application integration tasks, and provide early warnings. If a certain type of interface is found to have frequent compatibility issues in historical integrations, the system will automatically add relevant additional test items.

[0110] Based on the inspection results and risk warnings, a pre-deployment checklist is automatically generated. The checklist includes completed items, items awaiting confirmation, and risk items, facilitating the development team's final deployment preparations. If the deployment check passes, it indicates that the application integration task is problem-free, and the application integration process is complete after executing the task. If the deployment check fails, the application integration task needs to be corrected. This can be done manually or through intelligent correction based on risk points using a large-scale model. After correction, the application integration task is executed again, and the deployment check continues until it passes, completing the application integration process.

[0111] In the embodiments of this application, intelligent checks are achieved before production by checking access prerequisites and risk warnings. The risk warning mechanism can detect potential problems in advance, reduce the risk of production failure, and ensure the stability of the system launch.

[0112] For example, Figure 7 The diagram illustrates an automated processing flowchart of an application access method according to an embodiment of this application.

[0113] like Figure 7 As shown, a closed-loop processing framework of requirement analysis, knowledge reasoning, automated execution, and verification feedback is constructed to automatically complete application integration based on user-input access requirement data. When a user inputs an access requirement, the requirement is analyzed using a large model. Based on the analysis results and knowledge graph retrieval, an access solution is generated. The application integration task of the access solution is automatically executed. During execution, the specific process of the task is monitored in real time to check for anomalies. If an anomaly occurs, exception handling is performed, and the task continues to be executed while monitoring continues. If no anomalies occur, the task result is verified. If verification passes, a production check is performed. If verification fails, the task is corrected, and execution continues until verification passes. If the production check fails, the task is corrected, execution continues, and a production check is performed again until it passes. If the production check passes, the application integration is completed.

[0114] Based on the above application access method, this application also provides an application access device. The following will be combined with... Figure 8 The device is described in detail.

[0115] Figure 8 A schematic block diagram of an application access device according to an embodiment of this application is shown.

[0116] like Figure 8 As shown, the application access device 800 in this embodiment includes a semantic parsing module 810, a scheme generation module 820, an access task execution module 830, and an access task verification module 840.

[0117] The semantic parsing module 810 is used to perform semantic parsing on access requirement data based on a large model. In one embodiment, the semantic parsing module 810 can be used to perform the operation S210 described above, which will not be repeated here.

[0118] The scheme generation module 820 is used to retrieve the semantic parsing results based on the knowledge graph and the above-mentioned large model, and generate an application access scheme; wherein, the above-mentioned knowledge graph is constructed based on historical access data; in one embodiment, the scheme generation module 820 can be used to perform the operation S220 described above, which will not be repeated here.

[0119] The access task execution module 830 is used to execute the application access task in the above application access scheme according to the above knowledge graph, and to monitor the execution status of the above application access task in real time. In one embodiment, the access task execution module 830 can be used to execute the operation S230 described above, which will not be repeated here.

[0120] The access task verification module 840 is used to verify the execution result of the application access task based on the verification scheme. If the verification fails, the application access task is adjusted and executed until the verification is passed. The verification scheme is generated based on the semantic parsing results from the knowledge graph and is used for scenario analysis. In one embodiment, the access task verification module 840 can be used to execute the operation S240 described above, which will not be repeated here.

[0121] According to an embodiment of this application, the access task execution module 830 includes: a resource communication connection unit, configured to establish a communication connection with the resource management system through an application programming interface based on the application access task in the application access scheme; a template matching unit, configured to obtain a form template matching the resource application type in the application access task from the knowledge graph, wherein the form template includes a mapping relationship between form fields and entities; a parameter parsing unit, configured to parse the resource parameters in the application access task; a field matching unit, configured to perform field matching between the resource parameters and the form fields according to the entity mapping relationship; a parameter filling unit, configured to fill the matched resource parameters into the corresponding form fields based on the form template to generate a resource application form; and a form submission unit, configured to submit the resource application form to the resource management system through the application programming interface.

[0122] According to an embodiment of this application, the access task execution module 830 further includes: a firewall connection unit, used to establish a communication connection with the firewall management system through the application programming interface based on the application access task in the application access scheme; a rule acquisition unit, used to acquire firewall rule templates matching the business scenarios in the application access task from the knowledge graph; and a firewall activation unit, used to submit an activation application to the firewall management system through the application programming interface based on the firewall rule template.

[0123] According to an embodiment of this application, the semantic parsing module 810 includes: a semantic parsing unit, used to parse the access requirement data based on the large model and extract entity data and access key information; and an entity mapping unit, used to map the entities in the entity data to the entities in the knowledge graph.

[0124] According to an embodiment of this application, the semantic parsing result includes the entity data and the access key information; the scheme generation module 820 includes: a process element extraction unit, used to perform case retrieval on the entity data in the knowledge graph, determine similar historical cases, and extract process elements of the similar historical cases; a reasoning unit, used to reason about the process elements through the large model based on the access rules of the knowledge graph and the access key information to generate an initial access process; and an optimization unit, used to optimize the initial access process based on the resource configuration status to generate the application access scheme.

[0125] According to an embodiment of this application, the above-mentioned device 800 further includes: a call relationship processing module, used to collect call relationship information; wherein the call relationship information includes the caller, the callee, interface parameters and call frequency; and to generate a call relationship graph based on the knowledge graph according to the call relationship information.

[0126] According to an embodiment of this application, the access task verification module 840 includes: a verification scheme generation unit, configured to match the access key information with scene classification entities in the knowledge graph to determine similar scene categories, and determine a verification template based on the similar scene categories; and to adjust the verification template according to the access elements of the access key information through the large model to generate the verification scheme.

[0127] According to an embodiment of this application, the above-mentioned device 800 further includes: a risk inspection module, used to check the access preconditions of the above-mentioned application access task by traversing the dependency relationships of the above-mentioned knowledge graph if the verification is passed; and to identify the risk points of the above-mentioned application access task and to issue a risk warning based on the above-mentioned risk points.

[0128] According to an embodiment of this application, the above-mentioned access task execution module 830 further includes: a monitoring unit, used to monitor the execution status of the above-mentioned application access task in real time, wherein the execution status includes execution progress and execution status; a visualization unit, used to visualize the execution progress corresponding to the above-mentioned application access task; and an exception handling unit, used to trigger an exception handling mechanism when there is an exception in the execution status corresponding to the above-mentioned application access task, and adjust the above-mentioned application access task through the exception handling mechanism.

[0129] According to an embodiment of this application, the above-mentioned scheme generation module 820 further includes: a knowledge graph construction unit, used to construct an entity relationship graph based on a triplet structure; and to perform natural language processing on the above-mentioned historical access data, extract access entities and access relationships, and inject the above-mentioned access entities and access relationships into the above-mentioned entity relationship graph to construct the above-mentioned knowledge graph.

[0130] According to embodiments of this application, any multiple modules among the semantic parsing module 810, scheme generation module 820, access task execution module 830, access task verification module 840, call relationship processing module, and risk checking module can be merged into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this application, at least one of the semantic parsing module 810, scheme generation module 820, access task execution module 830, access task verification module 840, call relationship processing module, and risk checking module can be at least partially implemented as hardware circuits, such as field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits (ASICs), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuits, or implemented in software, hardware, and firmware, or in any appropriate combination of any of these three implementation methods. Alternatively, at least one of the semantic parsing module 810, the scheme generation module 820, the access task execution module 830, the access task verification module 840, the call relationship processing module, and the risk check module can be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0131] Figure 9 A block diagram schematically illustrates an electronic device suitable for implementing an application access method according to an embodiment of this application.

[0132] like Figure 9 As shown, an electronic device 900 according to an embodiment of this application includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage portion 908 into a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of this application.

[0133] RAM 903 stores various programs and data required for the operation of electronic device 900. Processor 901, ROM 902, and RAM 903 are interconnected via bus 904. Processor 901 executes various operations of the method flow according to embodiments of this application by executing programs in ROM 902 and / or RAM 903. It should be noted that the programs may also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 may also execute various operations of the method flow according to embodiments of this application by executing programs stored in said one or more memories.

[0134] According to embodiments of this application, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to a bus 904. The electronic device 900 may also include one or more of the following components connected to the input / output (I / O) interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 910 as needed so that computer programs read from it can be installed into the storage section 908 as needed.

[0135] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of this application.

[0136] According to embodiments of this application, the computer-readable storage medium can be a non-volatile computer-readable storage medium, such as including but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this application, the computer-readable storage medium may include ROM 902 and / or RAM 903 and / or one or more memories other than ROM 902 and RAM 903 described above.

[0137] Embodiments of this application also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the application access method provided in the embodiments of this application.

[0138] When the computer program is executed by the processor 901, it performs the functions defined in the system / apparatus of this application embodiment. According to the embodiments of this application, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0139] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and downloaded and installed via the communication section 909, and / or installed from a removable medium 911. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0140] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, it performs the functions defined in the system of this application embodiment. According to the embodiments of this application, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0141] According to embodiments of this application, program code for executing the computer programs provided in the embodiments of this application can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0142] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0143] Those skilled in the art will understand that the features described in the various embodiments of this application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this application. In particular, the features described in the various embodiments of this application can be combined and / or combined in various ways without departing from the spirit and teachings of this application. All such combinations and / or combinations fall within the scope of this application.

Claims

1. An application access method, characterized in that, The method includes: Semantic parsing of access requirement data is performed based on a large model; Based on the knowledge graph and the large model, the semantic parsing results are retrieved to generate an application access scheme; wherein, the knowledge graph is constructed based on historical access data; Execute application access tasks in the application access scheme based on the knowledge graph, and monitor the execution status of the application access tasks in real time; and The execution result of the application access task is verified based on the verification scheme. If the verification fails, the application access task is adjusted and the adjusted application access task is executed until the verification is passed. The verification scheme is generated by performing scenario analysis on the semantic parsing result based on the knowledge graph.

2. The method according to claim 1, characterized in that, The step of executing the application access task in the application access scheme based on the knowledge graph includes: Based on the application access task in the application access scheme, a communication connection is established with the resource management system through the application programming interface; Obtain a form template from the knowledge graph that matches the resource request type in the application access task, wherein the form template includes the mapping relationship between form fields and entities; Analyze the resource parameters in the application access task; Based on the entity mapping relationship, the resource parameters are matched with the form fields; Based on the form template, the matched resource parameters are filled into the corresponding form fields to generate a resource request form; and The resource request form is submitted to the resource management system through the application programming interface.

3. The method according to claim 2, characterized in that, The step of executing the application access task in the application access scheme based on the knowledge graph further includes: Based on the application access task in the application access scheme, a communication connection is established with the firewall management system through the application programming interface; Obtain firewall rule templates from the knowledge graph that match the business scenario in the application access task; and Based on the firewall rule template, an activation request is submitted to the firewall management system through the application programming interface.

4. The method according to claim 1, characterized in that, The semantic parsing of access requirement data based on the large model includes: Based on the large model, the access requirement data is analyzed to extract entity data and key access information; and The entities in the entity data are mapped to the entities in the knowledge graph.

5. The method according to claim 4, characterized in that, The semantic parsing result includes the entity data and the access key information; the step of retrieving the semantic parsing result based on the knowledge graph and the large model to generate an application access scheme includes: Case retrieval is performed on the entity data in the knowledge graph to identify similar historical cases and extract the process elements of the similar historical cases; Based on the access rules of the knowledge graph and the key access information, the initial access process is generated by reasoning about the process elements through the large model; and Based on the resource configuration status, the initial access process is optimized to generate the application access scheme.

6. The method according to claim 1, characterized in that, The method further includes: Collect call relationship information; wherein, the call relationship information includes the caller, the callee, interface parameters, and call frequency; and Based on the call relationship information, a call relationship graph is generated using the knowledge graph.

7. The method according to claim 4, characterized in that, The process of generating the verification scheme includes: The access key information is matched with scene classification entities in the knowledge graph to determine similar scene categories, and a verification template is determined based on the similar scene categories; and Based on the access elements of the key access information, the verification template is adjusted using the large model to generate the verification scheme.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: If the verification is successful, the preconditions for the application access task are checked by traversing the dependencies of the knowledge graph; and Identify the risk points of the application access task and issue risk warnings based on the risk points.

9. The method according to claim 1, characterized in that, The real-time monitoring of the execution status of the application access task includes: The execution status of the application access task is monitored in real time, wherein the execution status includes execution progress and execution status; Visualize the execution progress of the application access task; and If there is an abnormality in the execution status of the application access task, an exception handling mechanism is triggered to adjust the application access task.

10. The method according to claim 1, characterized in that, The process of constructing the knowledge graph includes: Constructing an entity relationship graph based on a triplet structure; and The historical access data is processed by natural language to extract access entities and access relationships, and the access entities and access relationships are injected into the entity relationship graph to construct the knowledge graph.

11. An application access device, characterized in that, The device includes: The semantic parsing module is used to perform semantic parsing on access requirement data based on a large model; The solution generation module is used to retrieve semantic parsing results based on the knowledge graph and the large model, and generate application access solutions; wherein, the knowledge graph is constructed based on historical access data; The access task execution module is used to execute application access tasks in the application access scheme based on the knowledge graph, and to monitor the execution status of the application access tasks in real time; and The access task verification module is used to verify the execution result of the application access task based on the verification scheme. If the verification fails, the application access task is adjusted and the adjusted application access task is executed until the verification is passed. The verification scheme is generated based on the semantic parsing result of the knowledge graph and scene analysis.

12. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 10.

14. A computer program product comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 10.

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