Intelligent shell command generation method and system under Linux environment

Abstract

This invention discloses a method for generating intelligent shell commands in a Linux environment, comprising: acquiring input natural language in a Linux terminal; determining whether a corresponding ready-made command exists; if it exists, directly executing the corresponding ready-made command; if it does not exist, calling a cloud model to perform semantic parsing on the natural language, identifying the user's intent based on the semantic parsing, and performing knowledge retrieval in conjunction with the context; combining the user's intent and the knowledge retrieval results, calling a large model to generate a shell command, and displaying the shell command to the user; after user confirmation, performing sandbox pre-execution; if pre-execution fails, returning the pre-execution result; if pre-execution succeeds, executing the shell command, and returning the execution result to the user after execution. This invention also discloses an intelligent shell command generation system in a Linux environment. This invention uses natural language to operate the terminal, making it convenient to use.

Description

Technical Field

[0001] This invention relates to a method for generating Shell commands, and more specifically, to a method for generating intelligent Shell commands in a Linux environment. Background Technology

[0002] In Linux / Unix operating systems, the Command-Line Interface (CLI), a text-based environment where users interact with the operating system kernel through the system's native terminal, is a core tool for system administrators, developers, and advanced users to perform system operations, automate tasks, and deploy software. To improve CLI efficiency, various auxiliary methods have been developed, primarily including traditional manual input, existing command assistance tools, and command generation scripts based on templates or fixed rules. However, these existing solutions all have significant limitations and cannot effectively meet users' needs to directly, accurately, and securely translate natural language intent into executable shell commands.

[0003] Option 1: Traditional manual input method. This method requires users to completely memorize and accurately input Shell commands and their complex parameter options. Its drawbacks are: 1) Steep learning curve: extremely unfriendly to non-professional users, requiring a significant amount of time to familiarize themselves with command syntax and parameter semantics; 2) High error-proneness: a high risk of operation failure due to command spelling errors, parameter order or format errors, potentially even leading to system security risks; 3) Low efficiency: performing complex tasks often requires manually combining multiple commands, a cumbersome process.

[0004] Option 2: Existing command assistance tools. These tools (such as fish-shell's intelligent auto-completion and tldr's simplified manual) provide limited contextual hints or simplified command examples during user input. Their limitations are: 1) Strong knowledge dependency: Users still need basic command knowledge as a starting point for input, and they cannot fundamentally understand the user's task intent described in natural language; 2) Lack of dynamic generation capability: Their functionality is essentially based on static knowledge base retrieval and hints, lacking the ability to dynamically generate complete command sequences based on complex and unforeseen scenarios; 3) No execution loop: They typically only provide hints and do not involve automatic command execution, result verification, or feedback optimization.

[0005] Option 3: Command generation script based on templates or fixed rules. This method matches user-input keywords using predefined rules or template libraries and outputs the corresponding commands. Its main problems are: 1) Poor flexibility: It can only handle limited scenarios predefined in the rule library, with weak generalization ability; 2) Lack of natural language understanding ability: It cannot effectively parse and understand the diversity, ambiguity, and contextual dependencies in natural language; 3) High maintenance cost: To cover more command and parameter combinations, a large rule library needs to be continuously expanded and maintained manually, making it unsustainable.

[0006] In summary, existing technical solutions have failed to achieve end-to-end intelligent transformation from the user's natural language description of the task intent to correct, executable, and context-appropriate Shell commands. Therefore, there is an urgent need for an intelligent method and system capable of deeply understanding user intent, dynamically generating and optimizing Shell commands, and forming an execution feedback loop to significantly lower the barrier to entry for using Linux Shell and improve operational efficiency and security. Summary of the Invention

[0007] To address the technical problem that existing technologies cannot achieve end-to-end intelligent conversion from user-described task intent in natural language to correct, executable, and context-compliant Shell commands, this invention provides an intelligent Shell command generation method in a Linux environment.

[0008] To solve the above technical problems, the technical solution adopted by this invention is to design a method for generating intelligent Shell commands in a Linux environment, including: Linux terminal for obtaining input natural language; Determine if a corresponding, directly executable command exists; If it exists, execute the corresponding existing command directly; If it does not exist, the cloud model is invoked to perform semantic parsing of the natural language, the user intent is identified based on the semantic parsing, and knowledge retrieval is performed in combination with the context; Based on the user intent and knowledge retrieval results, the large model is invoked to generate Shell commands, which are then displayed to the user. After user confirmation, sandbox pre-execution is performed; If the pre-execution fails, the pre-execution result is returned; If the pre-execution is successful, the Shell command will be executed, and the execution result will be returned to the user after execution. If the Shell command is executed successfully, the natural language, the corresponding semantic parsing, and the generated Shell command are stored in the experience pool; if the Shell command is executed unsuccessfully, the error is captured by the error handling module, the error is fed back to the user, and the Shell command generation strategy is optimized.

[0009] If the generated shell command contains the pattern rm -rf and the parameter is the root directory or the wildcard / *, it will be forcibly intercepted and the user will be prompted for confirmation.

[0010] If the execution of the Shell command fails due to the non-existence of the file directory, in addition to the error handling module capturing the error and reporting the error to the user, it is also recommended that the user create the directory first, and the directory check step will be automatically added to similar tasks in the future.

[0011] The determination of whether a corresponding ready-to-execute command exists includes: The conditional statement determines whether there is a corresponding executable command. If so, it determines whether there is a corresponding ready-made command that can be executed directly. It also matches the corresponding execution command through built-in commands or keywords. If a match is found, it determines that there is a ready-made command that can be executed directly.

[0012] The process of calling a cloud-based model to perform semantic analysis of the natural language, identifying user intent based on the semantic analysis, and performing knowledge retrieval in conjunction with the context includes: Call the cloud model API to perform semantic parsing of the natural language; The algorithm analyzes whether the user intends to optimize and organize files. If so, it returns the file path by calling the Prompt defined in the cloud model. Read the file as context, combine it with user input, call the API gateway address for load balancing, and call different Agent APIs; The Agent API identifies user intent and then performs knowledge retrieval based on the context.

[0013] The command to call the large model to generate a Shell includes: The large model is invoked using different prompts to generate shell commands; Determine the format of the Shell command and perform interactive selection using the interactive selection component; If the Shell command is in text format, the user is presented with the options to select, save, view, and exit through an interactive selection component. If the Shell command is in the form of a Shell script, the user is presented with selection, save, execute, and exit options through an interactive selection component. If the Shell command is in the form of an executable instruction, the user is presented with selection, direct execution, and exit options through an interactive selection component.

[0014] If the format of the Shell command is a Shell script, it also includes displaying the corresponding explanation of the Shell script to the user.

[0015] After executing the Shell command, a reinforcement learning algorithm is used, with the success rate of Shell command execution as the reward function, to enable the Agent to continuously evolve in its interaction with the real environment.

[0016] After the Linux terminal receives the input natural language, it also includes: The `trap` command intercepts Linux signals and jumps to determine if a corresponding, directly executable command exists.

[0017] The present invention also provides a smart shell command generation system in a Linux environment, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-described smart shell command generation method in a Linux environment.

[0018] This invention acquires input natural language through a Linux terminal and determines whether a corresponding, directly executable command exists. If it does, the corresponding command is executed directly. If not, a cloud-based model is invoked to perform semantic analysis on the natural language, identify the user's intent based on the semantic analysis, and perform knowledge retrieval in conjunction with the context. Combining the user's intent and the knowledge retrieval results, a large model is invoked to generate a Shell command, which is then displayed to the user. After user confirmation, a sandbox pre-execution is performed. If pre-execution fails, the pre-execution result is returned; if pre-execution succeeds, the Shell command is executed, and the execution result is returned to the user. This invention achieves end-to-end intelligent conversion from user-described task intents in natural language to correct, executable, and context-appropriate Shell commands. It effectively understands the complex operational intents expressed by users in natural language, automatically generates, verifies, executes, and optimizes corresponding Shell commands. By automatically converting natural language intents into executable Shell commands, it significantly reduces the difficulty of using the command line and improves the security and productivity of system operations. Furthermore, if the Shell command is executed successfully, the natural language, its corresponding semantic parsing, and the generated Shell command are stored in the experience pool. If the Shell command execution fails, the error is captured by the error handling module, the error is fed back to the user, and the Shell command generation strategy is optimized. Thus, through experience pool accumulation and error feedback optimization of the Shell command generation strategy, performance improves with use. Attached Figure Description

[0019] The present invention will now be described in detail with reference to the embodiments and accompanying drawings, wherein: Figure 1 This is a flowchart of the intelligent Shell command generation method under the Linux environment of the present invention. Detailed Implementation

[0020] The specific embodiments of the present invention are further described below with reference to the accompanying drawings: Please see also Figure 1 The intelligent shell command generation method in the Linux environment of this invention includes: Step 1: Obtain the input natural language in the Linux terminal.

[0021] Natural language can be input into the Linux terminal in various ways, such as via keyboard input.

[0022] For example, a user might type in shell-tai: "Find all the log files I modified today and compress them into a backup to the / home / backup directory."

[0023] Step 2: Determine if there is a corresponding ready-made command that can be executed directly.

[0024] Step 3: If it exists, execute the corresponding existing command directly.

[0025] If the input natural language is a simple, ready-made command that can be executed directly, then the corresponding ready-made command will be executed directly.

[0026] For example, you can use if or which statements to check if the script or command exists, then execute it directly and terminate. Alternatively, you can use built-in commands or keywords to match commands like source or export; if a match is found, execute it directly and terminate.

[0027] Step 4: If it does not exist, the cloud model is invoked to perform semantic parsing of the natural language, the user's intent is identified based on the semantic parsing, and knowledge retrieval is performed in combination with the context.

[0028] The system performs semantic parsing on the input natural language to identify its semantic meaning. Keywords are extracted from the natural language, and the task to be performed is identified based on these keywords.

[0029] If the input natural language does not have a readily executable command, the cloud model is invoked to perform semantic analysis on the natural language. Based on the semantic analysis, the user's intent is identified, and knowledge retrieval is performed in conjunction with the context. For example, if a user enters in shell-tai: "Find all the log files I modified today and package and back them up to the / home / backup directory," the semantics are complex, and there is no readily available command. In this case, it is judged as a complex task, requiring the cloud model to perform semantic analysis on the natural language, identify the semantics of the input natural language, and extract keywords such as "find files," "package and back up," "today," "log files," and " / home / backup." Based on the semantic analysis, the user's intent is identified, and knowledge retrieval is performed in conjunction with the context.

[0030] Step 5: Combining the user intent and knowledge retrieval results, call the large model to generate Shell commands and display the Shell commands to the user.

[0031] For example, when a user enters in shell-tai: "Find all the log files I modified today and compress and back them up to the / home / backup directory," the system uses the usage of the find and tar commands and the time parameter format from the knowledge base to generate a script containing the find command (with the -mtime 0 and -name "*.log" parameters) and the tar command. The exception handling module checks whether the target path exists and whether write permissions are available.

[0032] The system displays the generated script (with explanation) to the user: "I will perform the following operations: 1. Locate the .log files modified today in the current directory; 2. Package them into backup_$(date +%Y%m%d).tar.gz. Execute? (Y / N / Modify)" Step 6: After user confirmation, perform sandbox pre-execution.

[0033] Before actual execution, the command is "rehearsed" in a restricted containerized sandbox environment to check whether its file system operations, network access, and other behaviors exceed security policies. At the same time, high-risk operation patterns are hard-blocked using a static rule base.

[0034] Pre-execution in a sandbox allows you to verify the safety and correctness of the generated shell commands.

[0035] Step 7: If the pre-execution fails, return the pre-execution result.

[0036] If the pre-execution fails, the pre-execution result is returned, prompting the user to make modifications or take further intervention measures.

[0037] For example, if the task fails because / home / backup does not exist, the error handling module will capture the error, provide feedback to the user, and suggest creating the directory first. At the same time, the module update strategy will be optimized so that a directory check step can be automatically added to similar tasks in the future.

[0038] Step 8: If the pre-execution is successful, execute the Shell command and return the execution result to the user after execution.

[0039] If the pre-execution is successful, the shell command will be executed directly, and the execution result will be returned to the user so that the user can clearly understand the result. The execution result includes a success message or a successful result after error correction.

[0040] Step 9: If the Shell command is executed successfully, the natural language, the corresponding semantic parsing, and the generated Shell command are stored in the experience pool; if the Shell command is executed unsuccessfully, the error is captured using the error handling module, the error is fed back to the user, and the Shell command generation strategy is optimized.

[0041] This allows for the saving of correct Shell commands for quick recall and reference later. If the execution of a Shell command fails, the error is captured, reported back to the user, and the Shell command generation strategy is optimized.

[0042] This invention acquires input natural language through a Linux terminal and determines whether a corresponding, directly executable command exists. If it does, the corresponding command is executed directly. If not, a cloud-based model is invoked to perform semantic analysis on the natural language, identify the user's intent based on the semantic analysis, and perform knowledge retrieval in conjunction with the context. Combining the user's intent and the knowledge retrieval results, a large model is invoked to generate a Shell command, which is then displayed to the user. After user confirmation, a sandbox pre-execution is performed. If pre-execution fails, the pre-execution result is returned; if pre-execution succeeds, the Shell command is executed, and the execution result is returned to the user. This invention achieves end-to-end intelligent conversion from user-described task intents in natural language to correct, executable, and context-appropriate Shell commands. It effectively understands the complex operational intents expressed by users in natural language, automatically generates, verifies, executes, and optimizes corresponding Shell commands. By automatically converting natural language intents into executable Shell commands, it significantly reduces the difficulty of using the command line and improves the security and productivity of system operations. Furthermore, if the Shell command is executed successfully, the natural language, its corresponding semantic parsing, and the generated Shell command are stored in the experience pool. If the Shell command execution fails, the error is captured by the error handling module, the error is fed back to the user, and the Shell command generation strategy is optimized. Thus, through experience pool accumulation and error feedback optimization of the Shell command generation strategy, performance improves with use.

[0043] In this specific embodiment, if the execution of the Shell command fails due to the non-existence of the file directory, in addition to the error handling module capturing the error and reporting the error to the user, it also suggests that the user create the directory first, and automatically adds a directory check step to similar tasks in the future.

[0044] For example, if a user enters in the shell: "Find all the log files I modified today and compress them into a backup to the / home / backup directory," and the shell command fails to execute because the directory " / home / backup" does not exist, the system should not only report the error to the user but also suggest that the user create the directory. Furthermore, the system should automatically add a directory check step to similar tasks in the future. Alternatively, it could automatically generate the directory " / home / backup" before executing the command.

[0045] In this specific embodiment, determining whether there is a corresponding ready-made, directly executable command includes: The conditional statement determines whether there is a corresponding executable command. If so, it determines whether there is a corresponding ready-made command that can be executed directly. It also matches the corresponding execution command through built-in commands or keywords. If a match is found, it determines that there is a ready-made command that can be executed directly.

[0046] For example, you can use the shell's `if` and `which` statements to check if a script or command exists; if so, execute it directly and terminate the process. Alternatively, you can use built-in commands or keywords to match commands like `source` and `export`; if a match is found, execute it directly and terminate the process. The `export` command marks variables as environment variables, making them visible to the current shell and its child processes. The `source` command executes scripts or configuration files in the current shell environment without starting a new child process.

[0047] In this specific embodiment, the step of calling the cloud model to perform semantic parsing of the natural language, identifying the user's intent based on the semantic parsing, and performing knowledge retrieval in conjunction with the context includes: 1. The cloud-based model API (Application Programming Interface) is used to perform semantic parsing of the natural language. An Application Programming Interface (API) is a set of definitions and protocols created for interaction between different software components. An API defines how software modules interact with each other, including which functions can be called, how to call them, the required data format, and the form of the returned results. Through APIs, developers can reuse existing capabilities without understanding or modifying the underlying implementation details.

[0048] 2. Determine if the user intends to optimize and organize files. If so, return the file path by calling the Prompt defined in the cloud model. Prompts are the core input form for users to interact with large language models, guiding the model to generate expected content based on pre-trained knowledge.

[0049] 3. Read the file as context, combine it with user input, call the API gateway address for load balancing, and call different Agent APIs.

[0050] As a cloud-native API gateway, the API gateway's plugin system adopts a modular design, supporting the writing of custom plugins using the Lua language. Plugins run on the data plane (ETCD storage configuration, Worker process execution logic), and can intercept various stages of the request / response lifecycle.

[0051] An agent is an artificial intelligence entity capable of autonomously perceiving its environment, making decisions, and taking actions. The Agent API serves as a bridge connecting these agents to external systems, enabling developers to easily access the powerful functionalities of agents and automate and intelligentize workflows.

[0052] As a component of the monitoring system, the agent is responsible for collecting, reporting, and analyzing performance data from API interfaces. It can be deployed in the same environment as the service or run as a separate process.

[0053] 4. Identify user intent through the Agent API and then perform knowledge retrieval in conjunction with the context.

[0054] The Agent API calls user input as parameters, and the model performs intent recognition, identifying intents such as basic command generation, software installation, script optimization and generation, anomaly diagnosis, and security hardening. The Agent can be adjusted according to actual circumstances. Regardless of the identified intent, it performs knowledge retrieval based on the context.

[0055] In this specific embodiment, the step of calling the large model to generate a Shell includes: 1. Generate Shell commands by calling the large model through different prompts.

[0056] By combining intent and knowledge retrieval results, the large model is invoked through different prompts to generate results, which are essentially Shell commands.

[0057] 2. Determine the format of the Shell command and perform interactive selection using the interactive selection component.

[0058] Determine if the returned format is text, a shell script, or an executable command. The interactive selection component provides a rich set of command-line interface components, helping to build aesthetically pleasing and highly interactive CLI applications. Therefore, using the interactive selection component for interactive selection makes it convenient for users.

[0059] If the Shell command is in text format, the user is presented with the options to select, save, view, and exit through an interactive selection component. If the Shell command is in the form of a Shell script, the user is presented with selection, save, execute, and exit options through an interactive selection component. If the Shell command is in the form of an executable instruction, the user is presented with selection, direct execution, and exit options through an interactive selection component.

[0060] Specifically, if the Shell command format is a Shell script, the method also includes displaying an explanation of the Shell script to the user, further facilitating the user's understanding of the Shell script.

[0061] To enhance the agent's self-learning and evolution capabilities, after executing the Shell command, a reinforcement learning algorithm is used, with the success rate of Shell command execution as the reward function, enabling the agent to continuously evolve through interaction with the real environment.

[0062] We construct a triplet of "user intent - generated command - execution environment feedback" as training data. Using reinforcement learning algorithms such as PPO (Proximal Policy Optimization), and with command execution success rate as the reward function, the AI ​​Agent continuously evolves through interaction with the real environment, reducing the generation of syntactically correct but logically flawed commands.

[0063] Set up a simulated Linux Shell environment, allowing the agent to attempt to generate and execute commands within this environment. The policy is then optimized using a predefined reward function. For example, a command returning 0 earns a reward of +1, a non-zero return earns a reward of -1, and using a dangerous command earns a reward of -10.

[0064] In this specific embodiment, after the Linux terminal obtains the input natural language, it further includes: The `trap` command intercepts Linux signals and jumps to determine if a corresponding, directly executable command exists.

[0065] To enhance security, further security configurations can be made. In the rule base of the exception handling module, configure a rule: if the generated shell command contains the pattern `rm -rf` and the parameter is the root directory or the wildcard ` / *`, then forcibly intercept it and prompt the user for confirmation. `rm -rf` is a file deletion command in UNIX systems, meaning it silently and recursively deletes all currently accessed files. This command runs in a Linux system environment and can forcibly delete all files in the current directory, making them unrecoverable. By configuring rules to prevent accidental deletion, significant data loss due to accidental file deletion can be avoided.

[0066] Forced interception can be performed during the sandbox pre-execution process and then prompt the user for confirmation; or forced interception can be performed before the sandbox pre-execution and then prompt the user for confirmation.

[0067] The present invention relates to an intelligent Shell command generation system for a Linux environment, comprising a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the intelligent Shell command generation method for a Linux environment described above.

[0068] This invention has the following characteristics: 1. End-to-end conversion of natural language to executable code: It realizes the direct mapping from free-form language descriptions to accurate, executable system commands, solving the problem of the separation between intent understanding and code generation in traditional methods.

[0069] 2. Intelligent agent architecture that integrates static knowledge and dynamic learning: It combines a structured command knowledge base with dynamic policy optimization based on reinforcement learning, so that the system has both reliable basic knowledge and can adapt to new environments and personal habits.

[0070] 3. Execution of a closed-loop driven self-optimization mechanism: The actual execution results of commands are used as the core feedback signal to introduce optimization loops, so that model learning is not only based on text matching, but also on the effectiveness of actual operations, which greatly improves the reliability and practicality of the generated data.

[0071] 4. Context awareness and multi-turn interaction: The system can remember the dialogue context and system state, and support complex multi-turn clarification and improvement. For example, when a user says "use the result of the previous command to filter again", it provides a human-like interactive experience.

[0072] The present invention has the following advantages: 1. Significantly lower the barrier to entry: Non-professional users can operate complex systems using everyday language, while professional users can improve the efficiency of orchestrating complex tasks.

[0073] 2. Improve operational safety and accuracy: Through security audits and sandbox pre-execution, system risks caused by misoperation are effectively prevented, and optimization based on execution feedback reduces the generation of erroneous commands.

[0074] 3. Achieve quantitative efficiency improvement: Internal testing shows that when completing typical system management tasks (such as log analysis and batch file processing), the average time from conception to successful execution for users has been reduced by approximately 70%.

[0075] 4. Possesses adaptive and evolutionary capabilities: The system can continuously learn to adapt to individual user preferences and specific environments, providing increasingly personalized services that become more considerate with each use.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for generating intelligent Shell commands in a Linux environment, characterized in that... include: Linux terminal for obtaining input natural language; Determine if a corresponding, directly executable command exists; If it exists, execute the corresponding existing command directly; If it does not exist, the cloud model is invoked to perform semantic parsing of the natural language, the user intent is identified based on the semantic parsing, and knowledge retrieval is performed in combination with the context; Based on the user intent and knowledge retrieval results, the large model is invoked to generate Shell commands, which are then displayed to the user. After user confirmation, sandbox pre-execution is performed; If the pre-execution fails, the pre-execution result is returned; If the pre-execution is successful, the Shell command will be executed, and the execution result will be returned to the user after execution. If the Shell command is executed successfully, the natural language, the semantic parsing corresponding to the natural language, and the generated Shell command are stored in the experience pool. If the execution of the Shell command fails, the error is captured by the error handling module, the error is reported back to the user, and the Shell command generation strategy is optimized.

2. The method for generating intelligent Shell commands in a Linux environment according to claim 1, characterized in that: If the generated shell command contains the pattern rm -rf and the parameter is the root directory or the wildcard / *, it will be forcibly intercepted and the user will be prompted for confirmation.

3. The method for generating intelligent Shell commands in a Linux environment according to claim 2, characterized in that: If the execution of the Shell command fails due to the non-existence of the file directory, in addition to the error handling module capturing the error and reporting the error to the user, it is also recommended that the user create the directory first, and the directory check step will be automatically added to similar tasks in the future.

4. The method for generating intelligent Shell commands in a Linux environment according to claim 1, characterized in that: The determination of whether a corresponding ready-to-execute command exists includes: The conditional statement determines whether there is a corresponding executable command. If so, it determines whether there is a corresponding ready-made command that can be executed directly. It also matches the corresponding execution command through built-in commands or keywords. If a match is found, it determines that there is a ready-made command that can be executed directly.

5. The method for generating intelligent Shell commands in a Linux environment according to claim 1, characterized in that: The process of calling a cloud-based model to perform semantic analysis of the natural language, identifying user intent based on the semantic analysis, and performing knowledge retrieval in conjunction with the context includes: Call the cloud model API to perform semantic parsing of the natural language; The algorithm analyzes whether the user intends to optimize and organize files. If so, it returns the file path by calling the Prompt defined in the cloud model. Read the file as context, combine it with user input, call the API gateway address for load balancing, and call different Agent APIs; The Agent API identifies user intent and then performs knowledge retrieval based on the context.

6. The method for generating intelligent Shell commands in a Linux environment according to claim 5, characterized in that: The command to call the large model to generate a Shell includes: The large model is invoked using different prompts to generate shell commands; Determine the format of the Shell command and perform interactive selection using the interactive selection component; If the Shell command is in text format, the user is presented with the options to select, save, view, and exit through an interactive selection component. If the Shell command is in the form of a Shell script, the user is presented with selection, save, execute, and exit options through an interactive selection component. If the Shell command is in the form of an executable instruction, the user is presented with selection, direct execution, and exit options through an interactive selection component.

7. The method for generating intelligent Shell commands in a Linux environment according to claim 6, characterized in that: If the format of the Shell command is a Shell script, it also includes displaying the corresponding explanation of the Shell script to the user.

8. The method for generating intelligent Shell commands in a Linux environment according to claim 5, characterized in that: After executing the Shell command, a reinforcement learning algorithm is used, with the success rate of Shell command execution as the reward function, to enable the Agent to continuously evolve in its interaction with the real environment.

9. The method for generating intelligent Shell commands in a Linux environment according to claim 1, characterized in that: After the Linux terminal receives the input natural language, it also includes: The `trap` command intercepts Linux signals and jumps to determine if a corresponding, directly executable command exists.

10. A Linux-based intelligent shell command generation system, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the intelligent Shell command generation method in a Linux environment as described in any one of claims 1 to 9.

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