Development method and device based on AI model, electronic equipment and system

Abstract

The invention discloses a development method and device based on an AI model, electronic equipment and a system.The method comprises the steps that when a development requirement of a user is detected and it is determined that a target development environment meets development auxiliary conditions, the AI model analyzes the development requirement, and the development requirement is determined to meet the development auxiliary conditions; the method comprises the following steps: analyzing a to-be-executed operation, matching the analyzed to-be-executed operation with a plurality of predefined operations to obtain a demand processing result, transmitting a development demand to an MCP server by an AI model after determining that the demand processing result shows that the verification is passed, and executing a demand response operation on the development demand by the MCP server in combination with a local integration module. Returning a demand response result to the AI model for storage; wherein the demand response operation comprises a first response operation which is based on the MCP server and is used for determining a target project responding to the development demand, and a second response operation which is based on the local integration module and is used for processing an interaction item between the target project and the local file system. By implementing the method, the development efficiency and the code quality can be improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a development method and apparatus, electronic device and system based on AI models. Background Art

[0002] In the existing Xcode development environment, although it provides developers with a wealth of features and tools, developers still face a series of problems that urgently need to be solved during actual development. These problems affect development efficiency and project quality to some extent, and are specifically manifested as follows: Low coding efficiency: Developers need to manually complete a large amount of repetitive work, such as creating files and writing basic code structures. These repetitive tasks not only consume a lot of developers' time and energy, reducing overall development efficiency, but are also prone to errors due to human negligence, increasing the cost of subsequent debugging and maintenance.

[0003] Limited project analysis capabilities: As software projects grow in size and complexity, potential problems in the code become increasingly difficult to detect. While the existing Xcode development environment provides some basic code inspection features, it lacks intelligent project analysis tools. Developers struggle to quickly and comprehensively identify potential problems in the code, such as logical errors, performance bottlenecks, and non-compliance with coding standards.

[0004] Fragmented Development Environment: In recent years, artificial intelligence technology has been widely applied in software development. AI-assisted tools provide developers with powerful features such as automatic code generation and intelligent suggestions, helping to improve development efficiency and quality. However, in the existing Xcode development environment, there is a lack of effective integration mechanisms between AI-assisted tools and the local development environment. This forces developers to frequently switch between different tools and interfaces when using AI-assisted tools, resulting in cumbersome operations and an inability to achieve a seamless development experience. This fragmented development environment prevents developers from fully utilizing the advantages of AI-assisted tools, limiting the improvement of development efficiency and affecting the smoothness and convenience of the development process. Summary of the Invention

[0005] This invention provides a development method, apparatus, electronic device, and system based on AI models, which can improve development efficiency and code quality.

[0006] The first aspect of this invention discloses a development method based on an AI model, the method comprising: When a user's development request triggered by a target development environment is detected, and after determining that the target development environment currently meets the preset development assistance conditions, the AI ​​model performs a parsing operation on the development request to obtain the operation to be executed corresponding to the development request. The AI ​​model acquires multiple predefined operations; all predefined operations include at least one of file operations, project configuration queries, and build control; the file operations include any one of file read operations, file write operations, and file creation operations. The AI ​​model performs a matching process between the operation to be executed and all the predefined operations to obtain a matching result, which serves as the requirement processing result for the development requirement. Specifically, if the matching result indicates that a predefined operation among all the predefined operations matches the operation to be executed, then the development requirement is determined to have passed verification; if the matching result indicates that no predefined operation among all the predefined operations matches the operation to be executed, then the development requirement is determined to have failed verification. Once the requirement processing result indicates that the verification is successful, the AI ​​model transmits the development requirement to the MCP server. The MCP server, in conjunction with a preset local integration module, performs a requirement response operation on the development requirement to obtain a requirement response result. The MCP server then returns the requirement response result to the AI ​​model. The AI ​​model stores the requirement response result, or stores the operation record corresponding to the execution of the requirement response operation and the requirement response result. The requirement response operation includes a first response operation based on the MCP server and a second response operation based on the local integration module; the first response operation is used to at least determine the target project that responds to the development requirement; the second response operation is used to handle the interaction between the target project and the local file system. The step of the AI ​​model performing a parsing operation on the development requirements to obtain the corresponding operations to be executed includes: After receiving the development requirements, a preset AI model performs a parsing operation on the development requirements to obtain the corresponding operations to be executed. The AI ​​model acquires multiple predefined operations; all predefined operations include at least one of file operations, project configuration queries, and build control; the file operations include any one of file read operations, file write operations, and file creation operations. The AI ​​model performs a matching operation to be executed with all the predefined operations to obtain a matching result corresponding to the operation to be executed and all the predefined operations, which serves as the requirement processing result for the development requirements. Specifically, if the matching result indicates that there is a predefined operation among all the predefined operations that matches the operation to be executed, then the development requirement is determined to have passed the verification; if the matching result indicates that there is no predefined operation among all the predefined operations that matches the operation to be executed, then the development requirement is determined to have failed the verification.

[0007] As an optional implementation, in the first aspect of the present invention, the step of the MCP server, in conjunction with a preset local integration module, performing a requirement response operation on the development requirements to obtain a requirement response result for the development requirements includes: The MCP server performs a first response operation on the development requirement to obtain a first response result for the development requirement; the first response operation includes a protocol processing operation for processing the protocol corresponding to the development requirement, a security control operation for managing data interaction security, and a project management operation for managing the project context corresponding to the development requirement. A second response operation is performed on the first response result by a preset local integration module to obtain a second response result for the first response result, which serves as the requirement response result; the second response operation includes at least one of the following: execution of the target file, project construction management for the target project, and analysis and diagnosis of the target project.

[0008] As an optional implementation, in the first aspect of the present invention, the MCP server includes a protocol parser, a security controller, and a project manager; The step of the MCP server performing a first response operation on the development requirement to obtain a first response result for the development requirement includes: The protocol parser performs protocol processing operations on the development requirements to obtain protocol processing results for the development requirements. The protocol processing operations include protocol parsing, integrity checking, format validation, request routing, result formatting, and response. The request routing is used to forward the development requirements to a processing module capable of processing them. The result formatting and response are used to perform format conversion, response data packet generation, and response data packet transmission on the processing results returned by the processing module. The protocol processing results include the corresponding target data packet generated by the response data. The security controller performs security control operations on the protocol processing result to obtain a security control result for the protocol processing result; the security control operation includes permission and access scope checks, access control, and permission level management for the target data packet; the access control is used to restrict the access path and / or access file of the target data packet; The project manager performs project management operations on the security control result to obtain a project management result for the security control result, which serves as the first response result; wherein, the project management operation is at least used to determine the project context corresponding to the security control result.

[0009] As an optional implementation, in a first aspect of the present invention, the step of performing a second response operation on the first response result by a preset local integration module to obtain a second response result for the first response result includes: Since the preset local integration module determines the target interface corresponding to the first response result, the target interface includes any one of the following interfaces: file operation interface, build management interface, and project analysis interface; The local integration module determines the items to be executed in response to the first response result based on the target interface, and performs an item response operation that matches the items to be executed on the first response result to obtain the item response result in response to the first response result; When the target interface is the file operation interface, the response operation matching the item to be executed includes any one of the following operations: file reading and analysis operation, code file creation and modification operation, and file search operation. When the target interface is the build management interface, the response operation matching the item to be executed includes at least one of the following operations: accessing the target, configuration and scheme corresponding to the target project, executing a specific configuration build that matches the development requirements, running and managing a test suite that matches the development requirements, and obtaining and displaying build logs and error information. When the target interface is the project analysis interface, the response operation matching the item to be executed includes any one of the following operations: code problem analysis, code quality assessment, project configuration problem identification, and project structure report generation, which corresponds to the code to be analyzed corresponding to the development requirements.

[0010] As an optional implementation, in the first aspect of the present invention, the method further includes: When a user's development request for a target development environment is detected, it is determined whether the target development environment currently meets the preset development assistance conditions. When it is determined that the target development environment does not currently meet the preset development assistance conditions, a preset initialization operation is performed on the target development environment to obtain the initialization result for the target development environment; and after it is determined that the target development environment currently meets the development assistance conditions, the parsing operation of the development requirements by the AI ​​model is triggered to obtain the operation corresponding to the operation to be executed corresponding to the development requirements. The target development environment is the Xcode environment, and the initialization operation includes at least one of the following operations: MCP server startup operation, environment configuration operation for the target development environment, and communication link establishment operation based on the MCP server.

[0011] As an optional implementation, in the first aspect of the present invention, the step of returning the demand response result to the AI ​​model by the MCP server includes: The protocol parser performs formatting processing on the demand response result to obtain a formatted result corresponding to the demand response result; The MCP server, in conjunction with the encrypted communication method set by the MCP server, returns the formatted processing result to the AI ​​model.

[0012] A second aspect of this invention discloses a development apparatus based on an AI model, the development apparatus being used in the Xcode development tool, the development apparatus comprising: The AI ​​model is used to perform a parsing operation on the development requirement when it detects a development requirement triggered by a user for a target development environment, and after determining that the target development environment currently meets the preset development assistance conditions, to obtain the operation to be executed corresponding to the development requirement. The AI ​​model is also used to acquire multiple predefined operations; all the predefined operations include at least one of file operations, project configuration queries, and build control; the file operations include any one of file read operations, file write operations, and file creation operations. The AI ​​model is further used to match the operation to be executed with all the predefined operations to obtain a matching result corresponding to the operation to be executed and all the predefined operations, which serves as the requirement processing result for the development requirement; wherein, when the matching result indicates that there is a predefined operation among all the predefined operations that matches the operation to be executed, the development requirement is determined to have passed verification; when the matching result indicates that there is no predefined operation among all the predefined operations that matches the operation to be executed, the development requirement is determined to have failed verification. The AI ​​model is also used to transmit the development requirements to the MCP server after the requirement processing result indicates that the verification has been passed. The requirement response module is used by the MCP server in conjunction with a preset local integration module to perform requirement response operations on the development requirements and obtain requirement response results for the development requirements. The demand response module is also used to return the demand response result to the AI ​​model by the MCP server; The AI ​​model is also used to store the demand response result, or to store the operation record corresponding to the execution of the demand response operation and the demand response result; The requirement response operation includes a first response operation based on the MCP server and a second response operation based on the local integration module; the first response operation is used to at least determine the target project that responds to the development requirement; the second response operation is used to handle the interaction between the target project and the local file system.

[0013] As an optional implementation, in the second aspect of the present invention, the AI ​​model performs requirement processing operations on the development requirements to obtain requirement processing results for the development requirements, specifically including: After receiving the development requirements, a parsing operation is performed on the development requirements to obtain the corresponding operations to be executed. Obtain multiple predefined operations; all predefined operations include at least one of file operations, project configuration queries, and build control; the file operations include any one of file read operations, file write operations, and file creation operations; The operation to be executed is matched with all the predefined operations to obtain the matching results corresponding to the operation to be executed and all the predefined operations, which are used as the requirement processing results for the development requirements; Specifically, if the matching result indicates that there is a predefined operation among all the predefined operations that matches the operation to be executed, then the development requirement is determined to have passed the verification; if the matching result indicates that there is no predefined operation among all the predefined operations that matches the operation to be executed, then the development requirement is determined to have failed the verification.

[0014] As an optional implementation, in a second aspect of the invention, the demand response module includes the MCP server and the local integration module, wherein: The MCP server is used to perform a first response operation on the development requirement and obtain a first response result for the development requirement; the first response operation includes a protocol processing operation for processing the protocol corresponding to the development requirement, a security control operation for managing data interaction security, and a project management operation for managing the project context corresponding to the development requirement. The local integration module is used to perform a second response operation on the first response result to obtain a second response result for the first response result, which serves as the demand response result; the second response operation includes at least one of the following: execution of the target file, project construction management for the target project, and analysis and diagnosis of the target project.

[0015] As an optional implementation, in a second aspect of the present invention, the MCP server includes a protocol parser, a security controller, and a project manager; The protocol parser is used to perform protocol processing operations on the development requirements to obtain protocol processing results for the development requirements; the protocol processing operations include protocol parsing, integrity checking, format validation, request routing, result formatting, and response; the request routing is used to forward the development requirements to a processing module capable of processing the development requirements; the result formatting and response are used to perform format conversion, response data packet generation, and response data packet transmission on the processing results returned by the processing module; the protocol processing results include the corresponding target data packet generated by the response data. The security controller is used to perform security control operations on the protocol processing result to obtain a security control result for the protocol processing result; the security control operation includes permission and access scope checks, access control, and permission level management for the target data packet; the access control is used to restrict the access path and / or access file of the target data packet; The project manager is used to perform project management operations on the security control result to obtain a project management result for the security control result, which serves as the first response result; wherein, the project management operation is at least used to determine the project context corresponding to the security control result.

[0016] As an optional implementation, in a second aspect of the present invention, the local integration module performs a second response operation on the first response result to obtain a second response result for the first response result, specifically including: Since the preset local integration module determines the target interface corresponding to the first response result, the target interface includes any one of the following interfaces: file operation interface, build management interface, and project analysis interface; The local integration module determines the items to be executed in response to the first response result based on the target interface, and performs an item response operation that matches the items to be executed on the first response result to obtain the item response result in response to the first response result; When the target interface is the file operation interface, the response operation matching the item to be executed includes any one of the following operations: file reading and analysis operation, code file creation and modification operation, and file search operation. When the target interface is the build management interface, the response operation matching the item to be executed includes at least one of the following operations: accessing the target, configuration and scheme corresponding to the target project, executing a specific configuration build that matches the development requirements, running and managing a test suite that matches the development requirements, and obtaining and displaying build logs and error information. When the target interface is the project analysis interface, the response operation matching the item to be executed includes any one of the following operations: code problem analysis, code quality assessment, project configuration problem identification, and project structure report generation, which corresponds to the code to be analyzed corresponding to the development requirements.

[0017] As an optional implementation, in a second aspect of the invention, the development apparatus further includes: An initialization module is used to determine whether the target development environment currently meets preset development assistance conditions when a user triggers a development request for the target development environment. The initialization module is further configured to perform a preset initialization operation on the target development environment when it is determined that the target development environment does not currently meet the preset development assistance conditions, and obtain an initialization result for the target development environment; and after determining that the target development environment currently meets the development assistance conditions, trigger the execution of the parsing operation of the development requirements by the AI ​​model to obtain the operation corresponding to the operation to be executed corresponding to the development requirements; The target development environment is the Xcode environment, and the initialization operation includes at least one of the following operations: MCP server startup operation, environment configuration operation for the target development environment, and communication link establishment operation based on the MCP server.

[0018] As an optional implementation, in a second aspect of the present invention, the protocol parser is further configured to perform formatting processing on the demand response result to obtain a formatting processing result corresponding to the demand response result; The MCP server is also used to return the formatting processing result to the AI ​​model by combining the encrypted communication method set by the MCP server.

[0019] A third aspect of the present invention discloses an electronic device, the electronic device being equipped with the Xcode development tool as disclosed in the first aspect of the present invention, the Xcode development tool being used for developing applications; the electronic device is communicatively connected to a network attached storage device, and the electronic device accesses the network attached storage device through the application; Furthermore, the electronic device is used to perform some or all of the steps in the AI ​​model-based development method disclosed in the first aspect of the present invention.

[0020] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the AI ​​model-based development method disclosed in the first aspect of the present invention.

[0021] The fifth aspect of the present invention discloses an AI model-based development system, the development system comprising an AI model-based development apparatus as disclosed in the second aspect of the present invention, an electronic device having an application developed using the development apparatus installed, and a network-attached storage device communicatively connected to the electronic device; the development system is used to execute the AI ​​model-based development method as disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a development method, apparatus, electronic device, and system based on an AI model. By implementing this invention, an intelligent development mechanism linking the AI ​​model and the requirement processing module is established. Specifically, in the initial stage of triggering a development requirement, the AI ​​model can parse the requirement, transforming vague or complex requirement expressions into specific and clear operations to be executed. This process accurately captures user intent, avoiding development direction errors caused by misunderstandings of the requirements. Subsequently, by matching the parsed operations to be executed with predefined operations, a specific execution path is clarified for the development requirement. Then, through the collaborative work of the AI ​​model, the MCP server, and the local integration module, intelligent processing and efficient response to Xcode development requirements are achieved. Utilizing a distributed collaborative processing approach for development requirements optimizes development response speed, and through precise project positioning and local interaction guarantees, development efficiency and stability are improved. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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.

[0024] Figure 1 This is a flowchart illustrating a development method based on an AI model disclosed in an embodiment of the present invention; Figure 2 This is a flowchart illustrating another AI model-based development method disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a development device based on an AI model disclosed in an embodiment of the present invention; Figure 4This is a schematic diagram of another AI model-based development device disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an AI model-based development system disclosed in an embodiment of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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.

[0026] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0027] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0028] This invention discloses a development method, apparatus, electronic device, and system based on an AI model. It establishes an intelligent development mechanism linking the AI ​​model and a requirement processing module. Specifically, in the initial stage of triggering development requirements, the AI ​​model can parse the requirements, transforming vague or complex requirement expressions into specific and clear operations to be executed. This process accurately captures user intent, avoiding development direction errors caused by misunderstandings of requirements. Subsequently, by matching the parsed operations to be executed with predefined operations, a specific execution path is clarified for the development requirements. Then, through the collaborative work of the AI ​​model, the MCP server, and the local integration module, intelligent processing and efficient response to Xcode development requirements are achieved. Utilizing a distributed collaborative processing approach for development requirements optimizes development response speed, and through precise project positioning and local interaction guarantees, improves development efficiency and stability. These are described in detail below.

[0029] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a development method based on an AI model disclosed in an embodiment of the present invention. Wherein, Figure 1 The described AI model-based development method can be applied to the Xcode development tool, and also to AI model-based development devices; this embodiment of the invention is not limited thereto. Figure 1 As shown, this AI model-based development method may include the following operations: 101. When a user's development request triggered for the target development environment is detected, and after determining that the target development environment currently meets the preset development assistance conditions, the AI ​​model performs a parsing operation on the development request to obtain the corresponding operation to be executed.

[0030] In this embodiment of the invention, the target development environment can be the development environment corresponding to Xcode.

[0031] 102. The AI ​​model obtains multiple predefined operations; all predefined operations include at least one of the following: file operations, project configuration queries, and build control.

[0032] In this embodiment of the invention, file operations include any one of file read operations, file write operations, and file creation operations.

[0033] 103. The AI ​​model matches the operation to be executed with all predefined operations to obtain the matching results of the operation to be executed and all predefined operations, which serve as the requirement processing results for development needs.

[0034] In this embodiment of the invention, if the matching result indicates that there is a predefined operation among all predefined operations that matches the operation to be executed, then the development requirement is determined to have passed the verification; if the matching result indicates that there is no predefined operation among all predefined operations that matches the operation to be executed, then the development requirement is determined to have failed the verification.

[0035] In this embodiment of the invention, before performing step 102, the method further includes: The requirements processing operations to be performed include legality verification operations.

[0036] In this embodiment of the invention, a preset AI model is used to verify the legitimacy of user-triggered development requests. This allows for the identification and filtering of requests that do not conform to specifications or have potential problems at an early stage of the development process. This avoids problems such as repetitive work, wasted resources, and project delays caused by illegitimate requests during subsequent development, thereby improving development efficiency and quality.

[0037] In this embodiment of the invention, the requirement processing operation may further include a context isolation operation, which is used to maintain the independent state of each development requirement to prevent cross-request / cross-requirement pollution.

[0038] In this embodiment of the invention, in practical application, the AI ​​model can be set in a separate AI client layer. Specifically, the AI ​​model can be used as an AI assistant. The AI ​​assistant can adopt Claude or Cursor (both are artificial intelligence models). This embodiment of the invention does not limit the scope of the application.

[0039] In this optional embodiment, in practical applications, when the AI ​​model interacts with the MCP server, the MCP protocol restricts the AI ​​model to only perform predefined operations, thereby achieving precise and controllable overall process.

[0040] As can be seen, in this embodiment of the invention, the AI ​​model can parse development requirements, transforming vague or complex requirement expressions into specific and clear operations to be executed. This process accurately captures user intent, avoids development errors caused by misunderstandings of requirements, ensures that subsequent processing revolves around the user's true needs, and reduces ineffective development work. Then, by matching the parsed operations to be executed with predefined operations, a specific execution path is clarified for the development requirements. The predefined operations cover common and critical development operations such as file operations, project configuration queries, and build control, enabling development requirements to be mapped to executable steps, providing a clear foundation for subsequent legality verification and requirement response.

[0041] 104. Once the requirement processing result indicates that the verification has been passed, the AI ​​model transmits the development requirements to the MCP server. The MCP server, in conjunction with the preset local integration module, performs a requirement response operation on the development requirements to obtain the requirement response result for the development requirements. The MCP server then returns the requirement response result to the AI ​​model.

[0042] In this embodiment of the invention, the demand response operation includes a first response operation based on the MCP server and a second response operation based on the local integration module; the first response operation is used to at least determine the target project that responds to the development demand; the second response operation is used to handle the interaction between the target project and the local file system.

[0043] In this embodiment of the invention, the first response operation based on the MCP server focuses on identifying the target project that responds to development requirements. This helps developers quickly locate the project that needs to be operated on, reducing the time and effort spent searching for the target project among numerous projects. Meanwhile, the second response operation based on the local integration module specifically handles the interaction between the target project and the local file system, ensuring data consistency and operational accuracy during development, avoiding development anomalies caused by local environment issues, and further improving the stability and reliability of the development process.

[0044] 105. The AI ​​model stores the demand response results, or stores the operation records and demand response results corresponding to the execution of the demand response operation.

[0045] It is evident that implementation Figure 1 The described AI-based development method establishes an intelligent development mechanism that links the AI ​​model and the requirement processing module. Specifically, in the initial stage of triggering development requirements, the AI ​​model can parse the requirements, transforming vague or complex requirements into specific and clear actions to be performed. This process accurately captures user intent, avoiding development errors caused by misunderstandings of requirements. Then, by matching the parsed actions to be performed with predefined actions, a specific execution path is defined for the development requirements. Subsequently, through the collaborative work of the AI ​​model, the MCP server, and the local integration module, intelligent processing and efficient response to Xcode development requirements are achieved. Utilizing a distributed collaborative processing approach for development requirements optimizes development response speed, and through precise project positioning and local interaction guarantees, improves development efficiency and stability.

[0046] In an optional embodiment, the method by which the MCP server, in conjunction with a preset local integration module, performs a requirement response operation on the development requirements to obtain a requirement response result specifically includes: The MCP server performs a first response operation on the development requirements and obtains the first response result for the development requirements. The first response operation includes protocol processing operations for handling the protocol corresponding to the development requirements, security control operations for managing the security of data interaction, and project management operations for managing the project context corresponding to the development requirements. The preset local integration module performs a second response operation on the first response result to obtain a second response result for the first response result, which serves as the requirement response result. The second response operation includes at least one of the following: execution of the target file, project construction management for the target project, and analysis and diagnosis of the target project.

[0047] In this optional embodiment, the MCP server can handle the protocols corresponding to development requirements, ensuring that communication between the development environment, local integration modules, and other components follows a unified standard. In complex development environments, different components may be implemented by different teams or technology stacks. Protocol handling operations can avoid communication chaos caused by protocol differences, enabling development requirements to be accurately transmitted between components.

[0048] In this optional embodiment, the security control operations performed by the MCP server can manage data interaction, preventing sensitive data from being stolen or tampered with during transmission and processing. It can effectively resist external network attacks, such as preventing malicious requests from intruding into the development system, reducing project risks and data losses due to security vulnerabilities, and providing a safe and reliable environment for the development process.

[0049] In this optional embodiment, the project management operations performed by the MCP server can control the project context corresponding to development requirements, enabling the MCP server to accurately understand the current status and configuration information of the project. This helps to quickly locate relevant resources when processing development requirements, avoiding operational errors or inefficiencies caused by a lack of understanding of the project context. Simultaneously, based on the understanding of the project context, project management operations can rationally allocate server resources, such as computing resources and storage resources.

[0050] In this optional embodiment, the local integration module performs operations on the target files involved in the first response result, which can meet the processing needs of different types of files during development. Whether it is code files, configuration files, or resource files, corresponding operations can be performed according to development needs, such as compilation, parsing, and modification, thereby improving the flexibility of the development process.

[0051] In this optional embodiment, the analysis and diagnostic operations set for the target project can perform a comprehensive and in-depth analysis of the project, quickly locating problems such as code defects and performance bottlenecks. This helps developers take timely measures to resolve issues and reduce their impact on project schedule and quality. Furthermore, by analyzing project data, the local integration module can provide valuable suggestions for project optimization, such as code optimization suggestions and architecture adjustment suggestions. These suggestions help improve the project's performance, maintainability, and scalability, thereby enhancing the overall quality of the project.

[0052] As can be seen, in this optional embodiment, the MCP server and the local integration module perform different response operations, cooperating with each other to form an efficient demand response system. The MCP server is responsible for handling macro-level protocol, security, and project management, while the local integration module focuses on micro-level operations such as file operations, build management, and analysis and diagnosis for specific projects. This division of labor and cooperation can fully leverage the advantages of each component, improving the response speed and processing efficiency of development requirements.

[0053] In another alternative embodiment, the MCP server described above includes a protocol parser, a security controller, and a project manager; The methods described above, in which the MCP server performs the first response operation to the development request and obtains the first response result for the development request, specifically include: The protocol parser performs protocol processing operations on the development requirements to obtain the protocol processing results for the development requirements. The protocol processing operations include protocol parsing, integrity checking, format validation, request routing, result formatting, and response. Request routing is used to forward the development requirements to the processing modules that can handle them. Result formatting and response are used to convert the format of the processing results returned by the processing modules, generate response data packets, and transmit response data packets. The protocol processing results include the generation of corresponding target data packets along with the response data. The security controller performs security control operations on the protocol processing results to obtain security control results for the protocol processing results; the security control operations include permission and access scope checks for target data packets, access control, and permission level management; access control is used to restrict the access paths and / or access files of target data packets; The project manager performs project management operations on the security control results to obtain project management results for the security control results, which serve as the first response result; wherein, the project management operations are at least used to determine the project context corresponding to the security control results.

[0054] In this optional embodiment, the protocol parser performs protocol parsing on the development requirements, accurately parses the received data into understandable information, and avoids data misunderstandings caused by differences in protocol formats. Integrity checks ensure that the received data is not lost or corrupted during transmission, while format verification guarantees that the data conforms to the predetermined protocol format requirements.

[0055] In this optional embodiment, the request routing function can accurately forward development requests to the processing module capable of handling them. In complex development environments, multiple processing modules may be responsible for different functions. Request routing can allocate the development request to the most suitable processing module based on its specific content and type, avoiding resource waste and processing delays, and improving system processing efficiency.

[0056] In this optional embodiment, format conversion of the processing results returned by the processing modules can unify data of different formats generated by different processing modules into a standard format, facilitating subsequent processing and display. The generation and transmission of response data packets ensure that the processing results are returned to the client in a standardized manner, which improves system interactivity and user experience.

[0057] In this optional embodiment, the security controller performs permission and access scope checks and access control on the target data packet, ensuring that only users or modules with the appropriate permissions can access and process the target data packet. This effectively prevents the leakage and unauthorized access of sensitive data, ensuring the security and confidentiality of data during the development process.

[0058] In this optional embodiment, permission level management simplifies the process of permission allocation and management. Developers do not need to set permissions individually for each user or module; they can simply assign appropriate permission levels based on their roles, improving the efficiency and accuracy of permission management.

[0059] In this optional embodiment, the security control operation further includes data encryption with sandbox control, which is used to encrypt sensitive data; the sandbox control is used to restrict the operations performed on development requirements within a specific environment.

[0060] In this optional embodiment, the project management operations also include custom project selection, independent maintenance of workspace context, project structure management, project file management, and project configuration management.

[0061] In this optional embodiment, the project manager, by determining the project context corresponding to the security control results, can accurately understand the project's current status, configuration information, resource distribution, etc. This enables quick location of relevant project resources, such as code repositories, configuration files, and documentation, when handling development requirements, thus improving development efficiency. Furthermore, when handling multiple development projects simultaneously, the project manager can clearly distinguish the contexts of different projects, avoiding confusion and interference between them. Developers can quickly switch between different project contexts, facilitating multi-project management and improving the accuracy and stability of project management.

[0062] As can be seen, in this optional embodiment, the MCP server, through the collaborative work of the protocol parser, security controller, and project manager, achieves precise execution of first-response operations for development requirements. Specifically, the protocol parser ensures the accuracy and reliability of development requirements during communication, improving system compatibility and scalability; the security controller guarantees data security and system compliance, reducing security risks; and the project manager enables precise management and collaborative work on projects, improving development efficiency and quality.

[0063] In yet another optional embodiment, the method by which a preset local integration module performs a second response operation on the first response result to obtain a second response result specifically includes: Since the preset local integration module determines the target interface corresponding to receiving the first response result, the target interface includes any one of the following interfaces: file operation interface, build management interface, and project analysis interface; The local integration module determines the items to be executed in response to the first response result based on the target interface, and performs the item response operation that matches the items to be executed in response to the first response result, thereby obtaining the item response result in response to the first response result; When the target interface is a file operation interface, the response operation matching the item to be executed includes any one of the following operations: file reading and analysis operation, code file creation and modification operation, and file search operation. When the target interface is the build management interface, the response operation matching the item to be executed includes at least one of the following operations: accessing the target, configuration and scheme corresponding to the target project, executing a specific configuration build that matches the development requirements, running and managing a test suite that matches the development requirements, and obtaining and displaying build logs and error information. When the target interface is the project analysis interface, the response operation matching the item to be executed includes any one of the following operations: code problem analysis corresponding to the code to be analyzed corresponding to the development requirements, code quality assessment, project configuration problem identification, and project structure report generation.

[0064] In this optional embodiment, the local integration module may include multiple target interfaces, through which the local integration module directly interacts with the Xcode project (corresponding to the target project mentioned above) and the local file system.

[0065] In this optional embodiment, the local integration module can flexibly adapt to different types of development needs by determining the target interface corresponding to the first response result. The file operation interface, build management interface, and project analysis interface cover key aspects of the development process, allowing the local integration module to select the appropriate interface for processing based on specific requirements, thus improving the module's versatility and adaptability. Simultaneously, different interfaces correspond to different functional categories, allowing the local integration module to quickly locate the type of operation to be performed based on the target interface, avoiding blind and uncertain operations and improving the accuracy and efficiency of the operation.

[0066] In this optional embodiment, the file operation interface is used to handle file system read and write operations, and the supported operations include: reading and analyzing Swift, Objective-C and project configuration files; creating and modifying code files (including correct syntax handling and imports); intelligent file list (supporting type filtering and searching), etc.

[0067] In this optional embodiment, the build management interface is used to manage the build process of an Xcode project, and supports operations including: accessing project goals, configurations, and schemes; performing builds with specific configurations; running and managing test suites; and obtaining build logs and error information.

[0068] In this optional embodiment, the project analysis interface is used for project analysis and diagnostic capabilities, and supports operations including analyzing potential problems in the source code; providing code quality assessment; identifying project configuration issues; and generating project structure reports.

[0069] In this optional embodiment, it should be noted that the data layer to which the target project belongs is the Xcode project layer. As the final target of the above operations, it is characterized by the fact that access to the Xcode layer is local and does not depend on the network; and it can maintain the integrity and independence of the original project and support standard Xcode project operations.

[0070] In this optional embodiment, the AI ​​model, MCP server, and local integration module interact to assist in responding to development needs. The development is managed in a modular design, which allows new functions to be added in the corresponding module / end, thus improving the scalability of the overall method and system.

[0071] In this optional embodiment, the data flow direction of steps 101-103 in practical application is explained as follows: The AI ​​model (Claudee, Cursor) sends a request (such as "create a new view") via the MCP protocol. The MCP server receives and verifies the request. The security controller checks the permissions and access scope of the request. The project manager determines the project context required for the relevant operations to respond to the request. The file operation interface performs the actual file creation. The final result is formatted by the protocol parser and returned to the AI ​​model, which stores the returned final result. At the same time, the AI ​​model can also record the overall operation process.

[0072] As can be seen, in this optional embodiment, the local integration module achieves efficient processing of the first response result by determining the target interface, the task to be executed, and performing the matching task response operation. Specifically, the file operation interface, build management interface, and project analysis interface provide corresponding operational functions for different development needs, improving development efficiency and project quality. Furthermore, since the specific operation to ultimately respond to the development need is executed by the local integration module deployed locally—that is, all operations are completed locally—the code is not exposed to the internet, improving operational security.

[0073] Example 2 Please see Figure 2 , Figure 2 This is a flowchart illustrating another AI model-based development method disclosed in an embodiment of the present invention. Wherein, Figure 2 The described AI model-based development method can be applied to the Xcode development tool, and also to AI model-based development devices; this embodiment of the invention is not limited thereto. Figure 2 As shown, this AI model-based development method may include the following operations: 201. When a user triggers a development request for the target development environment, determine whether the target development environment currently meets the preset development support conditions.

[0074] 202. When it is determined that the target development environment does not currently meet the preset development auxiliary conditions, the preset initialization operation is performed on the target development environment to obtain the initialization result for the target development environment.

[0075] In this embodiment of the invention, the target development environment is an Xcode environment, and the initialization operation includes at least one of the following: an MCP server startup operation, an environment configuration operation for the target development environment, and a communication link establishment operation based on the MCP server. The order of the MCP server startup operation and the environment configuration operation for the target development environment can be adjusted according to actual circumstances; the communication link establishment operation based on the MCP server is executed when the MCP server is initially set up.

[0076] In this embodiment of the invention, the Node.js service is started via `node build / index.js`, loading the compiled TypeScript code to initiate the MCP server. For the environment configuration of the target development environment, automatic detection of Xcode projects (.xcodeproj or .xcworkspace) under the `PROJECTS_BASE_DIR` directory is configured. The aforementioned communication link establishment operation based on the MCP server can be implemented using inter-process communication (IPC) or local sockets, via encrypted communication using the MCP protocol (the documentation emphasizes "your code is not exposed to the internet").

[0077] 203. After determining that the target development environment currently meets the preset development support conditions, the AI ​​model performs a parsing operation on the development requirements to obtain the corresponding operations to be executed.

[0078] 204. The AI ​​model obtains multiple predefined operations; all predefined operations include at least one of the following: file operations, project configuration queries, and build control.

[0079] 205. The AI ​​model matches the operation to be executed with all predefined operations to obtain the matching results of the operation to be executed and all predefined operations, which serve as the requirement processing results for development needs.

[0080] 206. Once the requirement processing result indicates that the verification has been passed, the AI ​​model transmits the development requirements to the MCP server. The MCP server, in conjunction with the preset local integration module, performs a requirement response operation on the development requirements to obtain the requirement response result for the development requirements. The MCP server then returns the requirement response result to the AI ​​model.

[0081] 207. The AI ​​model stores the demand response results, or stores the operation records and demand response results corresponding to the execution of the demand response operation.

[0082] For further descriptions of steps 203-207 in this embodiment of the invention, please refer to the other specific descriptions of steps 101-105 in Embodiment 1. These descriptions will not be repeated in this embodiment of the invention.

[0083] It is evident that implementation Figure 2The described AI-based development method incorporates a stability and reliability assessment mechanism for the target development environment before responding to user-triggered development requests. This mechanism determines whether development support conditions are met, effectively preventing development failures caused by improper environment configuration, service shutdowns, or communication failures, ensuring the development process operates within a reliable environment. If the target development environment does not meet the support conditions, it automatically performs corresponding initialization operations, including starting the MCP server to ensure the core services of the development support functions operate normally, configuring environment parameters, and establishing communication links. This reduces the time and effort required for manual configuration by users, thus improving development efficiency to some extent.

[0084] In an optional embodiment, the method by which the MCP server returns the demand response result to the AI ​​model specifically includes: The protocol parser performs formatting processing on the demand response results to obtain the formatted results corresponding to the demand response results; The MCP server, in conjunction with the encrypted communication method set by the MCP server, returns the formatted processing results to the AI ​​model.

[0085] In this optional embodiment, the method further includes: After determining that the requirement response result is returned to the AI ​​model, persist the record of the project path associated with / activated by the requirement response result, cache all file structures accessed in response to the requirement response result, and track any unsaved modifications made during the generation of the requirement response result.

[0086] This operation enables intelligent status maintenance for each development requirement and allows for the retrospective function of operations after responding to each development requirement.

[0087] As can be seen, in this optional embodiment, the MCP server formats the demand response results through a protocol parser, achieving data standardization. It can also combine encrypted communication methods to return the results to the AI ​​model, achieving secure data transmission and efficient processing, and improving the system's compatibility, data quality, security, and stability.

[0088] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of a development apparatus based on an AI model, as disclosed in an embodiment of the present invention. This AI model-based development apparatus can be applied to the Xcode development tool, and can also be used to execute the AI ​​model-based development method described in Embodiment 1 or Embodiment 2 of the present invention. The embodiments of the present invention are not limited thereto. Figure 3As shown, the AI ​​model-based development device may include an AI model 301 and a demand response module 302, wherein: AI model 301 is used to perform a parsing operation on the development requirement when it detects a development requirement triggered by a user for the target development environment and after determining that the target development environment currently meets the preset development auxiliary conditions, so as to obtain the operation to be executed corresponding to the development requirement.

[0089] AI model 301 is also used to obtain multiple predefined operations; all predefined operations include at least one of file operations, project configuration queries, and build control; file operations include any one of file read operations, file write operations, and file creation operations.

[0090] AI model 301 is also used to match the operation to be executed with all predefined operations to obtain the matching results of the operation to be executed and all predefined operations, which serve as the requirement processing results for the development requirements. Specifically, if the matching result indicates that there is a predefined operation among all predefined operations that matches the operation to be executed, then the development requirement is determined to have passed the verification. If the matching result indicates that there is no predefined operation among all predefined operations that matches the operation to be executed, then the development requirement is determined to have failed the verification.

[0091] AI model 301 is also used to transmit development requirements to MCP server 3021 after the requirement processing result indicates that the requirement has been verified.

[0092] The requirement response module 302 is used by the MCP server 3021 in conjunction with the preset local integration module 3022 to perform requirement response operations on development requirements and obtain requirement response results for development requirements.

[0093] The demand response module 302 is also used to return the demand response results to the AI ​​model 301 by the MCP server 3021.

[0094] The requirement response operation includes a first response operation based on the MCP server 3021 and a second response operation based on the local integration module 3022; the first response operation is used to at least determine the target project for responding to the development requirements; the second response operation is used to handle the interaction between the target project and the local file system.

[0095] AI model 301 is also used to store demand response results, or to store operation records and demand response results corresponding to the execution of demand response operations.

[0096] It is evident that implementation Figure 3The described AI-based development device features an intelligent development mechanism that links the AI ​​model and the requirement processing module. Specifically, in the initial stage of triggering a development requirement, the AI ​​model parses the requirement, transforming vague or complex expressions into concrete and clear actions to be performed. This process accurately captures user intent, avoiding development errors caused by misunderstandings of the requirements. Then, by matching the parsed actions to be performed with predefined operations, a specific execution path is defined for the development requirement. Subsequently, through the collaborative work of the AI ​​model, the MCP server, and the local integration module, intelligent processing and efficient response to Xcode development requirements are achieved. Utilizing a distributed, collaborative approach to processing development requirements optimizes development response speed, and through precise project positioning and local interaction guarantees, development efficiency and stability are improved.

[0097] In an optional embodiment, the AI ​​model 301 performs requirement processing operations on the development requirements to obtain the requirement processing results for the development requirements, specifically including: After receiving the development requirements, a parsing operation is performed on the development requirements to obtain the corresponding operations to be executed; Retrieves multiple predefined operations; all predefined operations include at least one of file operations, project configuration queries, and build control; file operations include any one of file read operations, file write operations, and file creation operations; Match the operation to be executed with all predefined operations to obtain the matching results between the operation to be executed and all predefined operations, which serve as the requirement processing results for development needs; Specifically, if the matching result indicates that there is a predefined operation among all predefined operations that matches the operation to be executed, then the development requirement is determined to have passed the verification; if the matching result indicates that there is no predefined operation among all predefined operations that matches the operation to be executed, then the development requirement is determined to have failed the verification.

[0098] As can be seen, in this optional embodiment, the AI ​​model can parse development requirements, transforming vague or complex requirement expressions into specific and clear operations to be executed. This process accurately captures user intent, avoids development errors caused by misunderstandings of requirements, ensures that subsequent processing revolves around the user's true needs, and reduces ineffective development work. Then, by matching the parsed operations to be executed with predefined operations, a specific execution path is clarified for the development requirements. The predefined operations cover common and critical development operations such as file operations, project configuration queries, and build control, enabling development requirements to be mapped to executable steps, providing a clear foundation for subsequent legality verification and requirement response.

[0099] In another optional embodiment, the demand response module 302 includes an MCP server 3021 and a local integration module 3022, wherein: MCP server 3021 is used to perform first response operations on development requirements and obtain first response results for development requirements; the first response operations include protocol processing operations for handling the protocol corresponding to the development requirements, security control operations for managing data interaction security, and project management operations for managing the project context corresponding to the development requirements. The local integration module 3022 is used to perform a second response operation on the first response result to obtain a second response result for the first response result, which serves as the requirement response result. The second response operation includes at least one of the following: execution of the target file, project construction management for the target project, and analysis and diagnosis of the target project.

[0100] As can be seen, in this optional embodiment, the MCP server and the local integration module perform different response operations, cooperating with each other to form an efficient demand response system. The MCP server is responsible for handling macro-level protocol, security, and project management, while the local integration module focuses on micro-level operations such as file operations, build management, and analysis and diagnosis for specific projects. This division of labor and cooperation can fully leverage the advantages of each component, improving the response speed and processing efficiency of development requirements.

[0101] In yet another optional embodiment, the MCP server 3021 described above includes a protocol parser 30211, a security controller 30212, and a project manager 30213; Protocol parser 30211 is used to perform protocol processing operations on development requirements and obtain protocol processing results for those requirements. Protocol processing operations include protocol parsing, integrity checking, format validation, request routing, result formatting, and response. Request routing forwards development requirements to processing modules capable of handling them. Result formatting and response converts the format of the processing results returned by the processing modules, generates response data packets, and transmits response data packets. Protocol processing results and response data include the generation of corresponding target data packets. Security controller 30212 is used to perform security control operations on the protocol processing results to obtain security control results for the protocol processing results; the security control operations include permission and access scope checks for target data packets, access control, and permission level management; access control is used to restrict the access paths and / or access files of target data packets; Project Manager 30213 is used to perform project management operations on security control results and obtain project management results for the security control results as the first response result; wherein, the project management operations are used at least to determine the project context corresponding to the security control results.

[0102] As can be seen, in this optional embodiment, the MCP server, through the collaborative work of the protocol parser, security controller, and project manager, achieves precise execution of first-response operations for development requirements. Specifically, the protocol parser ensures the accuracy and reliability of development requirements during communication, improving system compatibility and scalability; the security controller guarantees data security and system compliance, reducing security risks; and the project manager enables precise management and collaborative work on projects, improving development efficiency and quality.

[0103] In another optional embodiment, the local integration module 3022 performs a second response operation on the first response result, and the specific method for obtaining the second response result for the first response result includes: Since the preset local integration module 3022 determines the target interface corresponding to receiving the first response result, the target interface includes any one of the following interfaces: file operation interface, build management interface, and project analysis interface; The local integration module 3022 determines the items to be executed in response to the first response result based on the target interface, and performs the item response operation that matches the items to be executed in response to the first response result, thereby obtaining the item response result in response to the first response result; When the target interface is a file operation interface, the response operation matching the item to be executed includes any one of the following operations: file reading and analysis operation, code file creation and modification operation, and file search operation. When the target interface is the build management interface, the response operation matching the item to be executed includes at least one of the following operations: accessing the target, configuration and scheme corresponding to the target project, executing a specific configuration build that matches the development requirements, running and managing a test suite that matches the development requirements, and obtaining and displaying build logs and error information. When the target interface is the project analysis interface, the response operation matching the item to be executed includes any one of the following operations: code problem analysis corresponding to the code to be analyzed corresponding to the development requirements, code quality assessment, project configuration problem identification, and project structure report generation.

[0104] As can be seen, in this optional embodiment, the local integration module achieves efficient processing of the first response result by determining the target interface, the items to be executed, and performing matching item response operations. Specifically, the file operation interface, build management interface, and project analysis interface provide corresponding operational functions for different development needs, improving development efficiency and project quality.

[0105] In yet another alternative embodiment, please refer to Figure 4 , Figure 4 This is a schematic diagram of another AI model-based development device disclosed in an embodiment of the present invention. Figure 4 As shown, the development device also includes an initialization module 303, wherein: The initialization module 303 is used to determine whether the target development environment currently meets the preset development assistance conditions when a user triggers a development requirement for the target development environment. The initialization module 303 is also used to perform a preset initialization operation on the target development environment when it is determined that the target development environment does not currently meet the preset development auxiliary conditions, and obtain the initialization result for the target development environment; and after determining that the target development environment currently meets the development auxiliary conditions, it triggers the AI ​​model 301 to perform the above-mentioned parsing operation of the development requirements by the AI ​​model, and obtain the operation corresponding to the operation to be executed corresponding to the development requirements. The target development environment is the Xcode environment, and the initialization operations include at least one of the following: starting the MCP server 3021, configuring the environment for the target development environment, and establishing a communication link based on the MCP server 3021.

[0106] As can be seen, in this optional embodiment, a mechanism for judging the stability and reliability of the target development environment is set up before responding to user-triggered development requests. This mechanism determines whether the development support conditions are met, effectively preventing development failures caused by improper development environment configuration, service failure, or communication link breakdowns, ensuring the development process takes place in a reliable environment. If the target development environment does not meet the development support conditions, corresponding initialization operations can be automatically executed, including starting the MCP server, ensuring the core services of the development support function operate normally, configuring environment parameters, and establishing communication links. This reduces the time and effort required for manual configuration by the user, and to some extent, improves development efficiency.

[0107] In another optional embodiment, the protocol parser 30211 is further configured to perform formatting processing on the demand response result to obtain a formatted processing result corresponding to the demand response result; The MCP server 3021 is also used to return the formatted processing result to the AI ​​model 301 in conjunction with the encrypted communication method set by the MCP server 3021.

[0108] As can be seen, in this optional embodiment, the MCP server formats the demand response results through a protocol parser, achieving data standardization. It can also combine encrypted communication methods to return the results to the AI ​​model, achieving secure data transmission and efficient processing, and improving the overall device's compatibility, data quality, security, and stability.

[0109] Example 4 Please see Figure 5 , Figure 5This is a schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. For example... Figure 5 As shown, the electronic device can be equipped with the Xcode development tool as described in Embodiment 1 of the present invention, which is used to develop applications; the electronic device is communicatively connected to a network attached storage device, and the electronic device accesses the network attached storage device through the application. Furthermore, the electronic device is used to perform some or all of the steps in the AI ​​model-based development method described in Embodiment 1 or Embodiment 2 of the present invention.

[0110] In this embodiment of the invention, the network-attached storage device is dedicated to storing network data. It can perform operations such as uploading, centralized storage, data sharing, backup, and recovery of interactive data generated when executing the steps of the AI ​​model-based development method described in Embodiment 1 or Embodiment 2 of the present invention. The network-attached storage device can also support multimedia services. The interactive data includes the aforementioned development requirements, operations to be executed, the multiple predefined operations, the matching results between the operations to be executed and all predefined operations, the requirement response results, and operation records corresponding to all operations executed during the process of executing the requirement response operation for the development requirements.

[0111] Example 5 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute some or all of the steps in the AI ​​model-based development method described in Embodiment 1 or Embodiment 2 of this invention.

[0112] Example 6 Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a development system based on an AI model disclosed in an embodiment of the present invention. For example... Figure 6 As shown, the development system includes an AI model-based development apparatus as disclosed in Embodiment 3 of the present invention, an electronic device with an application developed using the development apparatus installed, and a network-attached storage device communicatively connected to the electronic device; the development system is used to execute some or all of the steps in the AI ​​model-based development method described in Embodiment 1 or Embodiment 2 of the present invention.

[0113] Example 7 This invention discloses a computer program product, which includes a non-transitory computer storage medium storing a computer program, and the computer program is operable to cause a computer to perform some or all of the steps in the AI ​​model-based development method described in Embodiment 1 or Embodiment 2.

[0114] It should be noted that the device and system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0115] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0116] Finally, it should be noted that the above embodiments are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A development method based on an AI model, wherein the method is applied in the Xcode development tool, characterized in that, The method includes: When a user's development request triggered by the target development environment is detected, and after determining that the target development environment currently meets the preset development assistance conditions, the AI ​​model performs a parsing operation on the development request to obtain the operation to be executed corresponding to the development request. The AI ​​model acquires multiple predefined operations; all predefined operations include at least one of file operations, project configuration queries, and build control; the file operations include any one of file read operations, file write operations, and file creation operations. The AI ​​model performs a matching process between the operation to be executed and all the predefined operations to obtain a matching result, which serves as the requirement processing result for the development requirement. Specifically, if the matching result indicates that a predefined operation among all the predefined operations matches the operation to be executed, then the development requirement is determined to have passed verification; if the matching result indicates that no predefined operation among all the predefined operations matches the operation to be executed, then the development requirement is determined to have failed verification. Once the requirement processing result indicates successful verification, the AI ​​model transmits the development requirement to the MCP server. The MCP server, in conjunction with a pre-defined local integration module, performs a requirement response operation on the development requirement to obtain a requirement response result. The MCP server then returns the requirement response result to the AI ​​model, which stores the requirement response result, or stores the operation record corresponding to the requirement response operation and the requirement response result. The requirement response operation includes a first response operation based on the MCP server and a second response operation based on the local integration module. The first response operation is used to at least determine the target project responding to the development requirement. The second response operation is used to handle the interaction between the target project and the local file system.

2. The development method based on an AI model according to claim 1, characterized in that, The process of the MCP server, in conjunction with a pre-defined local integration module, performing a requirement response operation on the development requirements to obtain a requirement response result for the development requirements includes: The MCP server performs a first response operation on the development requirement to obtain a first response result for the development requirement; the first response operation includes a protocol processing operation for processing the protocol corresponding to the development requirement, a security control operation for managing data interaction security, and a project management operation for managing the project context corresponding to the development requirement. A second response operation is performed on the first response result by a preset local integration module to obtain a second response result for the first response result, which serves as the requirement response result; the second response operation includes at least one of the following: execution of the target file, project construction management for the target project, and analysis and diagnosis of the target project.

3. The development method based on an AI model according to claim 2, characterized in that, The MCP server includes a protocol parser, a security controller, and a project manager; The step of the MCP server performing a first response operation on the development requirement to obtain a first response result for the development requirement includes: The protocol parser performs protocol processing operations on the development requirements to obtain protocol processing results for the development requirements. The protocol processing operations include protocol parsing, integrity checking, format validation, request routing, result formatting, and response. The request routing is used to forward the development requirements to a processing module capable of processing them. The result formatting and response are used to perform format conversion, response data packet generation, and response data packet transmission on the processing results returned by the processing module. The protocol processing results include the corresponding target data packet generated by the response data. The security controller performs security control operations on the protocol processing result to obtain a security control result for the protocol processing result; the security control operation includes permission and access scope checks, access control, and permission level management for the target data packet; the access control is used to restrict the access path and / or access file of the target data packet; The project manager performs project management operations on the security control result to obtain a project management result for the security control result, which serves as the first response result; wherein, the project management operation is at least used to determine the project context corresponding to the security control result.

4. The development method based on an AI model according to claim 3, characterized in that, The step of performing a second response operation on the first response result by a preset local integration module to obtain a second response result for the first response result includes: Since the preset local integration module determines the target interface corresponding to the first response result, the target interface includes any one of the following interfaces: file operation interface, build management interface, and project analysis interface; The local integration module determines the items to be executed in response to the first response result based on the target interface, and performs an item response operation that matches the items to be executed on the first response result to obtain the item response result in response to the first response result; When the target interface is the file operation interface, the response operation matching the item to be executed includes any one of the following operations: file reading and analysis operation, code file creation and modification operation, and file search operation. When the target interface is the build management interface, the response operation matching the item to be executed includes at least one of the following operations: accessing the target, configuration and scheme corresponding to the target project, executing a specific configuration build that matches the development requirements, running and managing a test suite that matches the development requirements, and obtaining and displaying build logs and error information. When the target interface is the project analysis interface, the response operation matching the item to be executed includes any one of the following operations: code problem analysis, code quality assessment, project configuration problem identification, and project structure report generation, which corresponds to the code to be analyzed corresponding to the development requirements.

5. The development method based on an AI model according to any one of claims 1-4, characterized in that, The method further includes: When a user's development request for a target development environment is detected, it is determined whether the target development environment currently meets the preset development assistance conditions. When it is determined that the target development environment does not currently meet the preset development assistance conditions, a preset initialization operation is performed on the target development environment to obtain the initialization result for the target development environment; and after it is determined that the target development environment currently meets the development assistance conditions, the parsing operation of the development requirements by the AI ​​model is triggered to obtain the operation corresponding to the operation to be executed corresponding to the development requirements. The target development environment is the Xcode environment, and the initialization operation includes at least one of the following operations: MCP server startup operation, environment configuration operation for the target development environment, and communication link establishment operation based on the MCP server.

6. The development method based on an AI model according to claim 3 or 4, characterized in that, The step of the MCP server returning the demand response result to the AI ​​model includes: The protocol parser performs formatting processing on the demand response result to obtain a formatted result corresponding to the demand response result; The MCP server, in conjunction with the encrypted communication method set by the MCP server, returns the formatted processing result to the AI ​​model.

7. A development apparatus based on an AI model, wherein the development apparatus is used in the Xcode development tool, characterized in that, Its features are, The development apparatus includes: The AI ​​model is used to perform a parsing operation on the development requirement when it detects a development requirement triggered by a user for a target development environment, and after determining that the target development environment currently meets the preset development assistance conditions, to obtain the operation to be executed corresponding to the development requirement. The AI ​​model is also used to acquire multiple predefined operations; all the predefined operations include at least one of file operations, project configuration queries, and build control; the file operations include any one of file read operations, file write operations, and file creation operations. The AI ​​model is further used to match the operation to be executed with all the predefined operations to obtain a matching result corresponding to the operation to be executed and all the predefined operations, which serves as the requirement processing result for the development requirement; wherein, when the matching result indicates that there is a predefined operation among all the predefined operations that matches the operation to be executed, the development requirement is determined to have passed verification; when the matching result indicates that there is no predefined operation among all the predefined operations that matches the operation to be executed, the development requirement is determined to have failed verification. The AI ​​model is also used to transmit the development requirements to the MCP server after the requirement processing result indicates that the verification has been passed. The requirement response module is used by the MCP server in conjunction with a preset local integration module to perform requirement response operations on the development requirements and obtain requirement response results for the development requirements. The demand response module is also used to return the demand response result to the AI ​​model by the MCP server; The AI ​​model is also used to store the demand response result, or to store the operation record corresponding to the execution of the demand response operation and the demand response result; The requirement response operation includes a first response operation based on the MCP server and a second response operation based on the local integration module; the first response operation is used to at least determine the target project that responds to the development requirement; the second response operation is used to handle the interaction between the target project and the local file system.

8. An electronic device, characterized in that, The electronic device is equipped with the Xcode development tool as described in claim 1, the Xcode development tool being used to develop applications; the electronic device is communicatively connected to a network attached storage device, and the electronic device accesses the network attached storage device through the application. Furthermore, the electronic device is used to perform the AI ​​model-based development method as described in any one of claims 1-6.

9. A computer storage medium storing computer instructions, which, when invoked, are used to execute the AI ​​model-based development method as described in any one of claims 1-6.

10. A development system based on an AI model, characterized in that, The development system includes an AI model-based development apparatus as described in claim 7, an electronic device with an application developed using the development apparatus installed, and a network-attached storage device communicatively connected to the electronic device. The development system is used to execute the AI ​​model-based development method as described in any one of claims 1-6.

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