Intelligent configuration method and system based on LLM model

Abstract

The embodiment of the application discloses a kind of intelligent configuration method and system based on LLM model, the configuration intention description that the embodiment of the application obtains user inputs in natural language form;The configuration intention description is input into LLM model, and the preliminary configuration draft is generated by the large language model in combination with configuration knowledge base;The configuration draft is pre-verified by verification system;The configuration task that passes verification is issued to target node and executes configuration change;After configuration change execution, the verification system is verified, and verification result is generated.The application realizes the automatic conversion from natural language intention to machine executable configuration by introducing large language model, combined with the double security mechanism of pre-verification and post-verification, and optionally introducing artificial review and feedback learning loop, significantly improve the efficiency, accuracy and safety of configuration management, reduce the operation and maintenance threshold, realize the continuous accumulation and optimization of configuration knowledge.

Description

Technical Field

[0001] This invention relates to the field of computer software technology, specifically to an intelligent configuration method and system based on an LLM model. Background Technology

[0002] In the field of information technology, configuration management is a crucial element in ensuring the stable operation of network devices, server clusters, applications, and cloud infrastructure. Traditional configuration management methods mainly rely on operations and maintenance personnel manually writing configuration files or manually filling in configuration parameters through a graphical user interface (GUI). As systems grow in scale and become more complex, some enterprises have begun to adopt automated configuration management tools (such as Ansible, Puppet, SaltStack, etc.) to batch distribute pre-written configuration scripts to target nodes to achieve a certain degree of automated deployment and management.

[0003] However, existing configuration management technologies still have the following shortcomings in practical applications:

[0004] Configuration writing is both time-consuming and inefficient: Writing correct configuration scripts that conform to best practices requires operations and maintenance personnel to have in-depth domain knowledge, be familiar with the configuration syntax of specific tools or platforms, and invest a significant amount of time in writing and debugging. Manual operation is not only time-consuming and labor-intensive, but also prone to errors due to negligence, affecting system stability.

[0005] The gap between natural language intents and machine-executable configurations: Operations personnel typically use natural language to describe configuration requirements, such as "add a DNS record for a domain name" or "increase the load balancing weight of an application server." However, existing configuration management tools cannot directly understand such high-level intents and still require manual "translation" into specific configuration code or commands, lacking the ability to directly convert requirement descriptions into configuration generation.

[0006] Configuration verification is delayed and costly: In existing processes, configuration errors often only become apparent after deployment to test or production environments, leading to operational incidents such as service interruptions, rollbacks, or cascading failures. The cost of post-incident detection and remediation is exorbitant, and there is a lack of mechanisms for effective pre-verification of configuration content before deployment.

[0007] Difficulties in knowledge transfer and standardization: The configuration experience of senior operations and maintenance personnel is difficult to systematically accumulate and reuse; the configuration styles within the team are inconsistent; and the learning cycle for new members is long, which affects the overall operation and maintenance efficiency and the consistency of configuration, and is not conducive to the continuous accumulation of enterprise-level operation and maintenance management capabilities.

[0008] In summary, existing configuration management technologies have significant limitations in terms of intelligence, operational efficiency, security, and knowledge reuse. There is an urgent need for a system and method that can understand natural language intent, automatically generate and verify configurations, and has continuous learning capabilities to improve the overall performance and reliability of configuration management. Summary of the Invention

[0009] Therefore, embodiments of the present invention provide an intelligent configuration method and system based on an LLM model to solve the technical problems that existing technologies have significant limitations in terms of intelligence level, operational efficiency, security assurance, and knowledge reuse.

[0010] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0011] According to a first aspect of the present invention, an intelligent configuration method based on an LLM model is provided, the method comprising:

[0012] Obtain the configuration intent description input by the user in natural language;

[0013] The configuration intent description is input into a pre-built LLM model, which then combines the LLM model with a configuration knowledge base to generate a preliminary configuration draft.

[0014] The configuration draft is pre-verified by a verification system, and the pre-verification includes at least one or more of the following: syntax verification, security policy checking, and resource conflict analysis.

[0015] The verified configuration task is sent to the target node and the configuration changes are executed;

[0016] After the configuration change is executed, the verification system is used to perform post-event verification of the configuration effectiveness and service operation status, and generate verification results.

[0017] Furthermore, the method also includes:

[0018] After pre-verification, the configuration draft is manually reviewed, revised, or approved. The approved configuration version is locked and used for distribution.

[0019] Furthermore, the method also includes:

[0020] Collect manual review records, verification results, and actual operating status after configuration distribution as feedback data for fine-tuning and optimizing the large language model.

[0021] Furthermore, the configuration knowledge base includes one or more of the following: configuration templates, configuration skill sets, application programming interface information, and command line interface command information, which are used to assist the large language model in generating configuration data that conforms to the target system syntax.

[0022] Furthermore, the configuration draft is pre-validated by a verification system. This pre-validation includes at least one or more of the following: syntax checking, security policy checking, and resource conflict analysis.

[0023] Before the configuration is issued, the draft configuration is simulated, conflict detected, and compliance checked.

[0024] Furthermore, the verification system performs post-event verification of the configuration effectiveness and service operation status, including:

[0025] Verify whether the configuration is effective and whether the service is running as expected by actively probing or querying the status.

[0026] Furthermore, the verified configuration task is distributed to the target node and the configuration changes are executed, including:

[0027] A standardized configuration task format is adopted, and the format differences between the configuration draft and the downstream target node are decoupled through an adaptation layer to support heterogeneous resource configuration in multi-cloud or multi-environment environments.

[0028] Furthermore, the configuration knowledge base is embedded with security policies, cost optimization policies, or performance tuning specifications, enabling the large language model to automatically perform compliance checks and optimization suggestions when generating configuration drafts.

[0029] According to a second aspect of the present invention, an intelligent configuration system based on an LLM model is provided, the system comprising:

[0030] The user interface module is configured to receive configuration intent descriptions input by the user in natural language.

[0031] The LLM engine module is used to receive the configuration intent description and generate a preliminary configuration draft by combining it with the built-in configuration knowledge base;

[0032] The verification system module is used to pre-verify the configuration draft and perform post-verification after the configuration is issued;

[0033] The configuration execution engine module is used to distribute pre-verified configuration tasks to target nodes and execute configuration changes.

[0034] Furthermore, the user interface module is also configured to display the configuration draft, the manual review interface, the pre-verification results, and the post-verification results, and provide full-process visual traceability of the configuration task.

[0035] The embodiments of the present invention have the following advantages:

[0036] 1. Significantly improve configuration efficiency: Free up operations and maintenance personnel from the tedious and error-prone task of writing configuration scripts, allowing them to focus more on architecture design and high-level operations and maintenance decisions, reducing configuration generation time from hours / days to minutes.

[0037] 2. Reduce configuration errors and operational risks: By adhering to best practices through a large model, double verification by manual review, and the guarantee of an automated verification system, online failures caused by configuration errors are significantly reduced, and system stability is improved.

[0038] 3. Lowering the professional knowledge threshold: Junior engineers or developers can configure complex services using natural language, which reduces the learning curve of configuration management and promotes the average ability of the team.

[0039] 4. Achieve intelligent compliance and optimization of configuration: Security policies, cost optimization policies, performance tuning specifications, etc. can be embedded into the knowledge base or verification rules of the large model, and compliance checks and optimization suggestions can be automatically implemented during the configuration generation and verification stages.

[0040] 5. Forming Operation and Maintenance Knowledge Assets: The "prompt words-high-quality configuration" pairs accumulated by the system and the manual review records constitute a reusable and searchable configuration knowledge base, realizing the digital accumulation and inheritance of the team's operation and maintenance experience. Attached Figure Description

[0041] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary, and those skilled in the art can derive other embodiments based on the provided drawings without creative effort.

[0042] The structures, proportions, sizes, etc. illustrated in this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed herein, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0043] Figure 1 A schematic diagram of the logical structure of an intelligent configuration system based on an LLM model provided in an embodiment of the present invention;

[0044] Figure 2 A schematic diagram illustrating the application principle of an intelligent configuration system based on an LLM model, provided in an embodiment of the present invention;

[0045] Figure 3 This is a flowchart illustrating an intelligent configuration method based on an LLM model, provided in an embodiment of the present invention. Detailed Implementation

[0046] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0047] Currently, configuration management systems, such as those for network devices, server clusters, and applications, primarily rely on manually writing configuration files or manually configuring each item through a graphical user interface (GUI). Enterprises typically use dedicated systems or tools to distribute pre-written configuration scripts to target nodes to achieve automated deployment and management.

[0048] In order to address the technical problems that existing technologies have significant limitations in terms of intelligence level, operational efficiency, security and knowledge reuse.

[0049] refer to Figure 1 This invention discloses an intelligent configuration system based on the LLM model, which includes: a user interface module 1; an LLM engine module 2; a verification system module 3; and a configuration execution engine module 4.

[0050] User interface / tools: Receive prompts from users in natural language and display the configuration draft generated by the large model, the human review interface, and the final verification results.

[0051] Large Model Engine: The core processing module. It receives user prompts and, combined with built-in domain knowledge (such as configuration templates, configuration skills, API information, CLI command information, etc.) and contextual information (such as existing configurations), performs reasoning and deduction to generate preliminary configuration data that meets the requirements of the target system.

[0052] The verification system / tools provide two types of capabilities. The first is pre-verification: pre-verifying the configurations generated by the large model engine to determine the syntax correctness, security, etc. The second is the ability to verify the validity of the service after configuration.

[0053] Manual review and correction module: The generated configurations provide a point of human intervention. Reviewers can view, modify, approve, or reject the configurations generated by the large model. The approved configuration version will be recorded and can be used as feedback data to optimize the large model. This module is not mandatory. When the model is relatively mature and the correctness can be guaranteed by post-configuration verification without incurring losses, it can be directly deployed to the configuration execution engine for execution after pre-verification.

[0054] Configuration execution engine: responsible for securely and reliably sending approved standardized configuration tasks to the target node, and triggering service modules to load the configuration and provide services;

[0055] Feedback learning loop system: Collects correction records from "manual review", results from the "configuration verification system", and actual running status after configuration is issued, and uses them as feedback data to continuously fine-tune and optimize the generation quality and accuracy of the "large model configuration generation engine".

[0056] Corresponding to the aforementioned intelligent configuration system based on the LLM model, this invention also discloses an intelligent configuration method based on the LLM model. The following details the intelligent configuration method based on the LLM model disclosed in this invention, in conjunction with the aforementioned intelligent configuration system based on the LLM model.

[0057] refer to Figure 2 and Figure 3 This invention discloses an intelligent configuration method based on an LLM model, comprising:

[0058] Step 1: User Intent Input: Users input their intent to change the configuration through the interactive interface in natural language (i.e., prompts), and can specify constraints such as the target environment and service type.

[0059] Step 2: AI Configuration Generation: The large model engine receives user prompts and, combined with its built-in knowledge base and system context, automatically performs logical reasoning to generate a preliminary configuration draft (including specific configuration codes / commands and generation logic explanations).

[0060] Step 3: Configuration Pre-verification: The pre-verification module of the verification system simulates the operation of standardized configuration tasks and performs conflict detection (such as syntax verification, security policy check, resource configuration conflict analysis, etc.).

[0061] Step 4: (Optional) Manual Review and Revision: After pre-verification, the standardized configuration task enters the manual review queue. Reviewers conduct a final check on the logical rationality, security, and compliance of the configuration, and may modify, comment on, or approve it. The approved configuration version is locked and ready for execution. This step can be omitted when the system trust level is high enough.

[0062] Step 5: Configuration Distribution and Loading: The configuration execution engine distributes the locked configuration task to the specified target node or cloud platform, performs the actual configuration changes, and triggers the service to reload the configuration.

[0063] Step 6: Post-configuration verification: After the configuration change is executed, the post-configuration verification module of the verification system is immediately started. It verifies whether the new configuration has taken effect and whether the service is running as expected through active probing (such as ping, API call) or status query (such as checking service processes, viewing logs).

[0064] Step 7: Result Feedback and Learning: The configuration verification results (success / failure and details) are returned to the user interface for display. Simultaneously, the entire data chain generated throughout the process (including original prompts, generated configurations, manual correction records, pre / post-verification results, and service running status) is fully recorded and fed into the feedback learning loop system for iterative optimization of the large model engine.

[0065] The embodiments of the present invention have the following advantages:

[0066] 1. A large model configuration generation engine was introduced, which directly converts the user's natural language intent into machine-executable configuration code or commands, realizing an automated leap from "describing requirements" to "generating configurations".

[0067] 2. A closed-loop workflow of "generation-review-execution-verification" was designed. While leveraging the automatic generation capabilities of AI, manual review at key nodes ensures control and security, and automated verification ensures the correctness of the results.

[0068] 3. The configuration verification system has dual verification capabilities (pre-verification and post-verification), which significantly advances the discovery of configuration errors and enables rapid confirmation of the effects after changes, forming a closed loop for quality assurance.

[0069] 4. Configuration parsing and standardization services serve as an adaptation layer, decoupling the large model output from diverse downstream configuration execution tools, enabling the system to support heterogeneous resource configuration across multiple clouds and environments.

[0070] 5. A feedback learning loop was established, which utilizes human feedback and verification signals generated during the process to continuously iterate and optimize the generation quality and accuracy of the large model configuration generation engine, making the system more and more intelligent with use.

[0071] 6. The entire system emphasizes traceability. All prompts, generated configuration versions, audit records, execution logs, and verification results are fully recorded to meet audit and compliance requirements.

[0072] Taking the addition of an A record to the DNS system that resolves the domain name www.example.com to the IP address 1.1.1.1 as an example, the workflow of this invention will be explained in detail:

[0073] Step 1: The operations and maintenance personnel enter the following natural language prompt in the system interface: "Add an A record for the domain name www.example.com, pointing to the IP address 1.1.1.1, with a TTL of 300 seconds."

[0074] The second step involves the large model engine understanding user needs and, combined with its built-in DNS configuration knowledge (such as the configuration syntax of common DNS servers like BIND and PowerDNS), automatically generating a corresponding configuration draft. For example, for the BIND system, it generates a configuration code snippet similar to www.example.com.300INA1.1.1.1, and specifies which zone file this configuration should be added to.

[0075] Step 3: The verification system pre-verifies the task, checking the following: whether the syntax is correct, whether the IP address 1.1.1.1 is a valid public IP address, whether it conflicts with existing records (such as CNAME record conflicts), and whether the TTL value is within a reasonable range.

[0076] Step 4: After pre-verification is passed, the task will be presented on the review interface. Senior operations personnel will check whether the configuration content generated by AI is accurate (such as domain name spelling, IP address, and time), and confirm whether the target DNS server is correct. After confirming that everything is correct, click "Approve".

[0077] Step 5: After receiving the approval command, the configuration execution engine connects to the target DNS server via SSH or API, writes the generated A record content to the specified zone file, and executes rndcreload or an equivalent command to make the new configuration take effect.

[0078] Step 6: After the configuration is distributed, the verification system immediately initiates post-verification. It first uses the `dig` or `nslookup` command to query the A record of www.example.com to confirm that the resolution result is 1.1.1.1 and the TTL matches the settings. Next, it may further initiate queries from different network locations to verify that DNS recursive resolution has taken effect across the entire network.

[0079] Step 7: The user interface displays "DNS record added successfully, verification passed." Simultaneously, the complete data of this interaction (prompt words, generated BIND configuration, review record, and verified query results) is recorded in the feedback learning loop system. In the future, when users make requests like "add an A record to api.example.com pointing to 192.168.1.100," the large model can generate the configuration more accurately and quickly.

[0080] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A smart configuration method based on an LLM model, characterized in that, The method includes: Obtain the configuration intent description input by the user in natural language; The configuration intent description is input into a pre-built LLM model, which then combines the LLM model with a configuration knowledge base to generate a preliminary configuration draft. The configuration draft is pre-verified by a verification system, and the pre-verification includes at least one or more of the following: syntax verification, security policy checking, and resource conflict analysis. The verified configuration task is sent to the target node and the configuration changes are executed; After the configuration change is executed, the verification system is used to perform post-event verification of the configuration effectiveness and service operation status, and generate verification results.

2. The intelligent configuration method based on the LLM model as described in claim 1, characterized in that, The method further includes: After pre-verification, the configuration draft is manually reviewed, revised, or approved. The approved configuration version is locked and used for distribution.

3. The intelligent configuration method based on the LLM model as described in claim 2, characterized in that, The method further includes: Collect manual review records, verification results, and actual operating status after configuration distribution as feedback data for fine-tuning and optimizing the LLM model.

4. The intelligent configuration method based on the LLM model as described in claim 1, characterized in that, The configuration knowledge base includes one or more of the following: configuration templates, configuration skill sets, application programming interface information, and command line interface command information, which are used to assist the LLM model in generating configuration data that conforms to the target system syntax.

5. The intelligent configuration method based on the LLM model as described in claim 1, characterized in that, The configuration draft is pre-validated by a verification system. This pre-validation includes at least one or more of the following: syntax checking, security policy checking, and resource conflict analysis. Before the configuration is distributed, the draft configuration is simulated, conflict detected, and compliance checked.

6. The intelligent configuration method based on the LLM model as described in claim 1, characterized in that, The verification system performs post-event verification of the configuration effectiveness and service operation status, including: Verify whether the configuration is effective and whether the service is running as expected by actively probing or querying the status.

7. The intelligent configuration method based on the LLM model as described in claim 1, characterized in that, The verified configuration task is distributed to the target node and the configuration changes are executed, including: A standardized configuration task format is adopted, and the format differences between the configuration draft and the downstream target node are decoupled through an adaptation layer to support heterogeneous resource configuration in multi-cloud or multi-environment environments.

8. The intelligent configuration method based on the LLM model as described in claim 1, characterized in that, The configuration knowledge base is embedded with security policies, cost optimization policies, or performance tuning specifications, enabling the LLM model to automatically perform compliance checks and optimization suggestions when generating configuration drafts.

9. An intelligent configuration system based on an LLM model, characterized in that, The system includes: The user interface module is configured to receive configuration intent descriptions input by the user in natural language. The LLM engine module is used to receive the configuration intent description and generate a preliminary configuration draft by combining it with the built-in configuration knowledge base; The verification system module is used to pre-verify the configuration draft and perform post-verification after the configuration is issued; The configuration execution engine module is used to distribute pre-verified configuration tasks to target nodes and execute configuration changes.

10. The intelligent configuration system based on the LLM model as described in claim 9, characterized in that, The user interface module is also configured to display the configuration draft, the manual review interface, the pre-verification results, and the post-verification results, and to provide full-process visual traceability of the configuration task.

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