Architecture intelligent deployment system and method based on large language model

Abstract

The invention relates to the technical field of architecture deployment, in particular to an intelligent architecture deployment system and method based on a large language model. The method specifically comprises the steps that an information interaction module stores a deployment strategy rule set; the demand analysis module analyzes the deployment intention primitives through a preset mapping relation to generate a structured target blueprint; the scheme evaluation module calls a deployment strategy rule set to dynamically calculate the decision weight of the conflict identifier to generate a decision priority, generates a clear inquiry according to the decision priority, corrects a structured target blueprint, and iteratively generates a comprehensive evaluation result; the deployment decision-making module generates a candidate strategy matrix in combination with the multi-target tradeoff point and a preset strategy axis; performing predictive reasoning on the candidate strategy matrix, quantifying a risk adjustment utility score and outputting an optimal execution parameter set; and the scheme generation module outputs an architecture deployment scheme based on the optimal execution parameter set. According to the method, intelligent reasoning, scheme generation and prediction decision making are carried out by utilizing a large language model, and autonomous decision making and automatic generation of architecture deployment are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of architecture deployment, and in particular to an architecture intelligent deployment system and method based on a large language model. Background Art

[0002] In the deployment of enterprise-level software systems and cloud-native applications, designing a system architecture that precisely meets business needs while balancing multi-dimensional objectives is a technical challenge. Traditional architecture deployment processes rely on manual methods for requirements analysis, solution design, and configuration writing. This process is not only lengthy but also prone to errors and ambiguities amidst complex multi-party collaboration.

[0003] In existing technologies, although existing automation tools such as Infrastructure as Code have improved the efficiency of deployment execution, their functions are focused on executing a pre-defined architectural blueprint. Therefore, before this execution step, how to systematically convert high-level business requirements into specific architectural blueprints remains a key prerequisite in the deployment process. This step presents several technical issues: First, the process of converting unstructured business requirements into structured technical parameters relies on manual interpretation and definition, leaving room for optimization in terms of improving efficiency and consistency; second, architectural design requires a balance between multiple mutually constrained objectives such as performance, cost, and security, and systematically seeking optimization within a vast parameter combination space is a challenge; finally, comprehensive verification of the logical consistency and configuration rationality of the architectural solution before deployment to reduce subsequent adjustment costs is a technical direction that continues to receive attention in this field.

[0004] In summary, existing technologies have shortcomings in achieving an end-to-end process from intelligent analysis of business requirements, multi-objective trade-off decision-making, to automated generation of architectural solutions. To this end, a system and method for intelligent architecture deployment based on a large language model is proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide an architecture intelligent deployment system and method based on a large language model for architecture deployment. In order to solve the problems existing in the prior art, the present invention specifically includes: an information interaction module, which obtains architecture deployment requirements and stores a preset architecture knowledge base and deployment policy rule set; a requirement parsing module, which identifies architecture deployment requirements and extracts deployment intention primitives, and parses the deployment intention primitives through preset mapping relationships and the architecture knowledge base to generate a structured target blueprint; a solution evaluation module, which uses an audit language model to evaluate the structured target blueprint to identify conflict identifiers; calls the deployment policy rule set to dynamically calculate the arbitration weights of the conflict identifiers, and uses the arbitration weights to generate arbitration priorities; generates clarification queries based on the arbitration priorities to obtain feedback information, modifies the structured target blueprint based on the feedback information, triggers re-evaluation of the audit language model, and iteratively generates a comprehensive evaluation result; a deployment decision module, which uses a policy language model to identify multi-objective trade-offs between the comprehensive evaluation results and the structured target blueprint, and generates a candidate policy matrix by combining the multi-objective trade-offs and the preset policy axes; quantitatively generates risk-adjusted utility scores through predictive reasoning of the candidate policy matrix, and outputs an optimal execution parameter set based on the risk-adjusted utility scores; and a solution generation module, which outputs an architecture deployment solution based on the optimal execution parameter set. The present invention realizes autonomous decision-making and automatic generation of architecture deployment by utilizing large language model intelligent reasoning, solution generation and predictive decision-making, and solves the problem of low deployment efficiency caused by the inability to balance deployment costs, system response speed and dynamic business changes.

[0006] To achieve the above object, the present invention provides the following technical solutions: An architecture intelligent deployment system based on a large language model, comprising: Information interaction module, which obtains architecture deployment requirements and stores the preset architecture knowledge base and deployment strategy rule set; The requirement parsing module identifies architecture deployment requirements and extracts deployment intent primitives. Through the preset mapping relationship and architecture knowledge base, it parses the deployment intent primitives to generate a structured target blueprint. The solution evaluation module uses the audit language model to evaluate the structured target blueprint and identify conflict identifiers; it calls the deployment policy rule set to dynamically calculate the adjudication weights of the conflict identifiers and uses the adjudication weights to generate adjudication priorities; it generates clarification queries based on the adjudication priorities to obtain feedback information, and based on the feedback information, it modifies the structured target blueprint, triggers re-evaluation of the audit language model, and iteratively generates comprehensive evaluation results; Deploy a decision-making module, using a policy language model to identify multi-objective trade-offs between the comprehensive assessment results and the structured goal blueprint, and generate a candidate policy matrix by combining the multi-objective trade-offs with the preset policy axes. Through predictive reasoning on the candidate policy matrix, a risk-adjusted utility score is quantitatively generated, and the optimal execution parameter set is output based on the risk-adjusted utility score. The solution generation module outputs the architecture deployment solution based on the optimal execution parameter set.

[0007] Preferably, the information interaction module includes: the architecture knowledge base includes best practice patterns, component templates, performance benchmarks and constraint rules; the deployment strategy rule set includes the predefined sorting rules of the decision priority.

[0008] Preferably, the requirement parsing module includes a natural language processing unit and a preset mapping database; the natural language processing unit identifies and extracts deployment intention primitives including performance, cost and security based on the architecture deployment requirements through natural language processing technology; the preset mapping database stores the mapping relationship between the deployment intention primitives and quantitative technical indicators, the quantitative technical indicators including target response delay and budget upper limit, and the deployment intention primitives are converted into quantitative technical indicators based on the mapping relationship; based on the architecture knowledge base, constraint rules related to quantitative technical indicators are retrieved, and a structured target blueprint including constraint boundaries is constructed in combination with the constraint rules and technical indicators.

[0009] Preferably, the solution evaluation module includes: dynamically constructing the architecture to be tested based on the structured target blueprint and the component template; calling the audit language model to compare and analyze the architecture to be tested with the performance benchmark and constraint rules, and generating an initial comprehensive evaluation result including potential risk points and conflict identifiers, where the conflict identifier is used to indicate that there is a logical conflict between the architecture to be tested and the constraint rules; when there is a conflict identifier in the initial comprehensive evaluation result, parsing the conflict identifier to obtain the conflict domain and conflict type of the conflict identifier, calling the deployment strategy rule set in combination with the conflict type, dynamically calculating the arbitration weight of the conflict identifier, sorting the conflict identifiers based on the arbitration weight, and generating an arbitration priority; based on the arbitration priority, constructing and issuing a clarification inquiry containing a recommended solution to obtain feedback information; based on the feedback information, correcting the parameters of the structured target blueprint; and re-triggering the audit language model for re-evaluation based on the corrected structured target blueprint until a comprehensive evaluation result is generated in which no conflict identifier is present.

[0010] Preferably, the deployment decision module includes: using a policy language model to perform semantic association and pattern matching on the comprehensive evaluation results and the structured target blueprint to locate multi-objective trade-off points; based on the multi-objective trade-off points, deconstructing the best practice pattern as an infrastructure component; generating a baseline architecture solution by adaptively integrating the infrastructure components, generating candidate architecture adjustment solutions based on the multi-dimensional transformation of the baseline architecture solution based on a preset policy axis, and constructing a candidate policy matrix based on the candidate architecture adjustment solutions; performing predictive reasoning on the candidate policy matrix and generating an estimated evaluation report; calculating the risk-adjusted utility score of the candidate architecture adjustment solution based on the estimated evaluation report, ranking and deciding based on the risk-adjusted utility score, and outputting the optimal execution parameter set.

[0011] Preferably, the solution generation module includes: using a preset generator plug-in library to map the optimal execution parameter set into an architecture configuration code, generating a deployment configuration file based on the architecture configuration code; generating a natural language argument based on full-link data; and outputting an architecture deployment solution including the deployment configuration file and the natural language argument.

[0012] Preferably, the natural language argument includes: integrating full-link data including architecture deployment requirements, structured target blueprints, comprehensive evaluation results and optimal execution parameter sets; reasoning the full-link data based on a preset interpretation task prompt template to generate a natural language argument including a summary of potential risk points, a description of multi-objective trade-offs and reasons for adopting the optimal execution parameter set.

[0013] A method for intelligent architecture deployment based on a large language model, comprising: Obtain architecture deployment requirements, store preset architecture knowledge base and deployment strategy rule sets; Identify architecture deployment requirements and extract deployment intent primitives. Analyze deployment intent primitives through preset mapping relationships and architecture knowledge base to generate a structured target blueprint. Using the audit language model to evaluate the structured target blueprint and identify conflict identifiers; calling the deployment policy rule set to dynamically calculate the adjudication weights of the conflict identifiers, and using the adjudication weights to generate adjudication priorities; generating clarification inquiries based on the adjudication priorities to obtain feedback information, and revising the structured target blueprint based on the feedback information, triggering re-evaluation of the audit language model, and iteratively generating comprehensive evaluation results; Utilize the policy language model to identify the multi-objective trade-offs between the comprehensive assessment results and the structured goal blueprint, and combine the multi-objective trade-offs with the preset policy axes to generate a candidate policy matrix. Through predictive reasoning of the candidate policy matrix, quantitatively generate risk-adjusted utility scores, and output the optimal execution parameter set based on the risk-adjusted utility scores. Output architecture deployment plan based on the optimal execution parameter set.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention identifies and extracts deployment intention primitives in requirements, converts them into quantitative technical indicators with specific numerical values ​​using preset mapping relationships, and retrieves relevant constraint rules from the architecture knowledge base for integration, thereby constructing an automated conversion mechanism that accurately quantifies unstructured deployment intentions into structured technical blueprints. Through this mechanism, the system systematically parses and constructs the originally vague business language into a machine-readable structured target blueprint with clear constraint boundaries and a structured target blueprint. This method replaces the traditional process of interpreting and translating requirements that relies on manual experience, and avoids the overall errors in design direction that may be caused by subjective understanding deviations. It provides an unambiguous single fact for subsequent automated evaluation and decision-making processes, ensuring the input quality of the entire intelligent deployment system and the accuracy of the final results.

[0015] 2. The present invention establishes a closed loop of risk diagnosis and solution correction for human-machine collaboration by deeply integrating feedback information into the automated evaluation process. After using the audit language model to identify conflict identifiers, the system calls the deployment policy rule set to dynamically calculate the conflict's adjudication weight, and automatically constructs clarification inquiries containing recommended solutions based on the generated adjudication priority. After receiving user feedback, the solution evaluation module can accurately correct the structured target blueprint and trigger the audit language model to iteratively re-evaluate until there are no conflicts in the solution. This mechanism transforms the solution review process that relies on manual experience into a standardized, traceable, interactive audit process. By achieving effective risk convergence in the early stages of decision-making, it ensures that the solution input into the final decision stage is consistent with the user's true intentions, thereby improving the completeness and reliability of the decision-making basis.

[0016] 3. The present invention deploys decision modules to build a closed-loop decision engine that integrates derivative solution generation and predictive quantitative decision-making, realizing a full-process innovation from "option creation" to "optimal choice". The engine first combines multi-objective trade-off points with preset strategic axes to derivatively generate a candidate solution matrix rich in strategic diversity, and performs in-depth predictive reasoning on each candidate solution in the candidate solution matrix. By generating a multi-dimensional estimated evaluation report, each candidate solution is quantified into a precise risk-adjusted utility score. This decision-making process that seamlessly connects solution generation and quantitative evaluation transforms qualitative comparisons that rely on expert experience into a scientific decision-making paradigm driven by data, broadens the breadth and cutting-edge nature of decision options, improves the objectivity, reproducibility and high credibility of the final decision, and enhances the flexibility and optimization potential of architectural design. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a structural diagram of an architecture intelligent deployment system based on a large language model of the present invention; Figure 2 This is a workflow diagram of the solution evaluation module of the present invention; Figure 3 A workflow diagram for deploying the decision-making module of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] See also Figures 1 to 3 The present invention provides an architecture intelligent deployment system and method based on a large language model. The technical solution is as follows: An architecture intelligent deployment system based on a large language model, including the following modules: Information interaction module, which obtains architecture deployment requirements and stores the preset architecture knowledge base and deployment strategy rule set; The requirement parsing module identifies architecture deployment requirements and extracts deployment intent primitives. Through the preset mapping relationship and architecture knowledge base, it parses the deployment intent primitives to generate a structured target blueprint. The solution evaluation module uses the audit language model to evaluate the structured target blueprint and identify conflict identifiers; it calls the deployment policy rule set to dynamically calculate the adjudication weights of the conflict identifiers and uses the adjudication weights to generate adjudication priorities; it generates clarification queries based on the adjudication priorities to obtain feedback information, and based on the feedback information, it modifies the structured target blueprint, triggers re-evaluation of the audit language model, and iteratively generates comprehensive evaluation results; Deploy a decision-making module, using a policy language model to identify multi-objective trade-offs between the comprehensive assessment results and the structured goal blueprint, and generate a candidate policy matrix by combining the multi-objective trade-offs with the preset policy axes. Through predictive reasoning on the candidate policy matrix, a risk-adjusted utility score is quantitatively generated, and the optimal execution parameter set is output based on the risk-adjusted utility score. The solution generation module outputs the architecture deployment solution based on the optimal execution parameter set.

[0020] Example 1: This embodiment provides a specific application of an architecture intelligent deployment system based on a large language model. Its typical application scenario is that e-commerce company A needs to quickly deploy a backend service architecture for an online platform to cope with high concurrent traffic during promotional activities. An architecture intelligent deployment system based on a large language model is introduced.

[0021] See Figure 1 , the system proposed by the present invention specifically includes: An architecture intelligent deployment system based on a large language model, comprising: Information interaction module, which obtains architecture deployment requirements and stores the preset architecture knowledge base and deployment strategy rule set; The requirement parsing module identifies architecture deployment requirements and extracts deployment intent primitives. Through the preset mapping relationship and architecture knowledge base, it parses the deployment intent primitives to generate a structured target blueprint. The solution evaluation module uses the audit language model to evaluate the structured target blueprint and identify conflict identifiers; it calls the deployment policy rule set to dynamically calculate the adjudication weights of the conflict identifiers and uses the adjudication weights to generate adjudication priorities; it generates clarification queries based on the adjudication priorities to obtain feedback information, and based on the feedback information, it modifies the structured target blueprint, triggers re-evaluation of the audit language model, and iteratively generates comprehensive evaluation results; Deploy a decision-making module, using a policy language model to identify multi-objective trade-offs between the comprehensive assessment results and the structured goal blueprint, and generate a candidate policy matrix by combining the multi-objective trade-offs with the preset policy axes. Through predictive reasoning on the candidate policy matrix, a risk-adjusted utility score is quantitatively generated, and the optimal execution parameter set is output based on the risk-adjusted utility score. The solution generation module outputs the architecture deployment solution based on the optimal execution parameter set.

[0022] Furthermore, the acquisition of architecture deployment requirements, storage of a preset architecture knowledge base and deployment strategy rule set, and the corresponding information interaction module of the system specifically include: E-commerce company A interacts with the system through an information interaction module, which integrates a hybrid input interface and an interaction processing unit. The hybrid input interface is configured to receive architecture deployment requirements input from a terminal. These requirements include unstructured requirements in natural language format and structured requirements with preset parameters. For example, in the case of unstructured requirements, e-commerce company A can enter a natural language requirement description through the text input box in the hybrid input interface. For example, e-commerce company A may enter the following: "Design a backend service architecture for a new e-commerce platform that can handle burst traffic, guarantee 99.95% availability, and keep initial monthly costs within 3,000 yuan." In the case of structured requirements, e-commerce company A can submit a structured requirement defined by preset parameters through a form in the hybrid input interface. For example, the following parameters may be set: "Service type: e-commerce," "Availability index: 99.95%," "Monthly budget (yuan): ≤ 3,000 yuan," and "Target environment: AWS (Amazon Web Services)."

[0023] The interaction processing unit is configured to manage and coordinate the complete conversation process. The specific workflow is as follows: Step S201: receiving initial requirements; the hybrid input interface receives the user's initial architecture deployment requirements and passes them to the requirements parsing module for preliminary parsing.

[0024] Step S202: Outputting a clarification query. If the requirement parsing module identifies ambiguous or conflicting semantics within the requirement, for example, if the specific metric for "burst traffic" is undefined, it generates corresponding system interaction information. The interaction processing unit receives the system interaction information, formats it into a clarification query, and outputs the clarification query to the terminal via the interface. For example, the output might read: "We have received a request. To better quantify burst traffic, please estimate the peak number of queries per second (QPS)."

[0025] Step S203: receiving feedback: after seeing the clarification inquiry on the interface, e-commerce company A enters feedback information, for example, "Peak QPS is expected to be 2000."

[0026] Step S204: Processing continuous input: The interaction processing unit takes the feedback information as a continuous input and passes it to the demand analysis module for the next round of processing.

[0027] The information interaction module stores a preset architecture knowledge base and a set of deployment policy rules. The architecture knowledge base consists of a document database and a vector database, and contains best practice patterns, component templates, constraint rules, and performance benchmarks. Component templates and constraint rules are defined in a structured format and stored in a document database; the component template is defined as a structured text object containing clear fields, such as component identification, cloud service provider, a list of configurable parameters and their default values ​​and legal value ranges; the constraint rule is defined as a structured object containing a rule ID, trigger conditions, and mandatory assertions. For example, a constraint rule may be "Rule ID-SEC01: For any e-commerce architecture involving payment functions, its database components must enable static encryption." For the best practice model, during the system initialization phase, the unstructured documents and semi-structured data in the knowledge base are fed into a vectorized processing engine. The processing engine uses a text embedding model to process the knowledge content into high-dimensional feature vectors, establishes an index relationship between the feature vectors and the original knowledge text, and ultimately stores the index relationship in a vector database. For example, a best practice model might be a solution called "High-Availability Elastic E-Commerce Backend Model," which defines the core elements of a load balancer, an application server auto-scaling group, and a cross-availability zone database. The performance benchmark stores performance data for different components under specific loads. For example, under a specific application load, a single instance of the "Amazon EC2 t3.Medium" component can stably process approximately 800QPS of requests.

[0028] The deployment policy rule set defines the arbitration priorities between different types of requirements. For example, this is defined through an ordered priority list, where "security compliance" is prioritized, followed by "availability," "performance," and finally "cost." When the system detects a conflict between a user's preference for low cost and a security compliance constraint, it enforces the security compliance rule based on this priority list.

[0029] The information interaction module also dynamically records the session history generated by user input, including the complete interaction sequence, terminal configuration preferences, and target environment. The complete interaction sequence records the question-and-answer dialogue between the user and the system. For example, the system output is "Please estimate peak QPS," and the terminal input is "Peak QPS is expected to be 2000." By parsing the complete interaction sequence, the terminal configuration preferences are extracted. For example, if e-commerce company A chooses "availability" in a clarification query between "cost" and "availability," it is recorded as "availability takes precedence over cost." The target environment is the deployment target platform determined based on user requirements, such as AWS (Amazon Web Services).

[0030] By pre-setting an architectural knowledge base containing best practices and constraint rules and defining clear decision priority rules, it provides a professional and reliable data and logical foundation for the system's automated evaluation and decision-making, ensuring the compliance of output plans and consistency of decisions.

[0031] Furthermore, the identification of architecture deployment requirements extracts deployment intention primitives, and through the preset mapping relationship and architecture knowledge base, the deployment intention primitives are parsed to generate a structured target blueprint, corresponding to the system's requirement parsing module, specifically including: After receiving the architecture deployment requirements and session history information, the requirements parsing module uses natural language processing technology to identify and extract deployment intention primitives in the architecture deployment requirement text, where the deployment intention primitives include performance, availability, cost, and security.

[0032] The deployment intent primitives are converted into quantifiable, structured goal blueprints using a predefined mapping database. This mapping database is configured based on expert experience, and the mapping rules in the database contain detailed structured information. Specifically, these include: a unique intent identifier; other intent types with trade-offs associated with the intent, such as "cost-sensitive"; specific quantitative technical indicators, such as "availability percentage" and "deployment mode"; these quantitative technical indicators are accompanied by target values, acceptable ranges, and default values; and mapping rules are linked to relevant mandatory constraints in the architectural knowledge base. For example, for the deployment intent primitive "high availability," its mapped structured object values ​​include "availability level not less than 99.95%," "deployment mode is cross-zone," "disaster recovery strategy is default-off," and a "trade-off reminder" field, with the trade-off reminder being "this option will increase costs." When the system identifies deployment intent primitives for "high availability" and "cost-sensitive," it merges the corresponding indicator lists into a unified preliminary set of technical indicators based on the priority levels predefined in the deployment policy rule set. The specific arbitration merging logic is as follows: the system first retrieves the technical indicators corresponding to the intent primitives. For conflicting indicators, it queries the priority preset for the corresponding conflicting intent primitives in the deployment strategy rule set, and automatically adopts the indicator value corresponding to the intent primitive with a higher priority to form a unified preliminary technical indicator set.

[0033] Based on the mapping database, default metrics are retrieved and instantiated and overwritten, combining these default metrics with session history information to generate the final quantitative target. During this instantiation and overwriting process, the system follows a preset priority sequence: the parameter values ​​explicitly specified by e-commerce company A in its structured requirements have the highest priority and directly override all default metrics; the terminal setting preferences parsed from session history have the second highest priority; if neither of these two explicit specifications is specified, the default values ​​from the mapping database are used. For example, for performance, "peak QPS expected to be 2000" is converted to "throughput target: >= 2000 QPS"; for availability, "availability level: >= 99.95%, deployment mode: cross-availability zone"; and for cost, "budget cap (yuan): <= 3000." Based on the structured target blueprint, the parsing unit retrieves relevant constraint rules, such as rule ID-SEC01, from the architectural knowledge base of the information interaction module. These constraints are combined with the structured target blueprint to ultimately construct and output a structured target blueprint containing the complete constraint boundaries. By automatically converting ambiguous natural language requirements into quantitative, structured target blueprints with clear constraints, ambiguity and deviations that may arise from manual interpretation are eliminated at the source, providing accurate and consistent input for subsequent automated processes, and ensuring that the final solution accurately responds to user intent.

[0034] During the output of the structured target blueprint, a multi-source parameter consistency check is performed. Specifically, for the same technical indicator, parameter values ​​are extracted based on the architecture deployment requirements, which include both unstructured and structured requirements. The extracted parameter values ​​are then compared to detect any parameter conflicts. When a parameter conflict is identified, the conflict is resolved based on the pre-set data source clarity adjudication rules. These rules quantify clarity scores for parameter values ​​from different sources and adopt the parameter value with the highest score to resolve the conflict. When parameter values ​​have the same clarity score, a clarification query containing the conflicting parameter values ​​is generated for correction. By proactively cross-validating user input from different sources and quantifying clarity scores, the efficiency of requirement resolution is improved and the reliability of the final output solution is enhanced.

[0035] Furthermore, the audit language model is used to evaluate the structured target blueprint to identify conflict identifiers; the deployment policy rule set is called to dynamically calculate the decision weights of the conflict identifiers, and the decision weights are used to generate decision priorities; clarification inquiries are generated according to the decision priorities to obtain feedback information, and the structured target blueprint is modified based on the feedback information, triggering re-evaluation of the audit language model, and iteratively generating comprehensive evaluation results. The corresponding system solution evaluation module specifically includes: See Figure 2 After receiving the structured target blueprint, the solution evaluation module starts the process of generating the initial architecture information to be tested. This process specifically includes extracting the core feature tags in the structured target blueprint, such as service type: e-commerce, availability level: >99.9%; matching and retrieving the core feature tags based on the architecture knowledge base to locate the most relevant best practice mode, such as the high-availability elastic e-commerce backend mode; the system traverses the component list defined by the best practice mode and loads the corresponding component templates for the core elements in the list. If a core element corresponds to multiple optional component templates, the system will give priority to the template that meets the core constraints and has the lowest estimated cost for instantiation and combination; the system preliminarily assigns template parameters according to the specific values ​​in the structured target blueprint, such as the budget cap and performance target. If there is no direct correspondence, the system adopts the default value defined in the template; the instantiated components are combined into machine-readable architecture information to be tested, and the architecture information to be tested includes its topology and configuration.

[0036] An assessment prompt is generated, containing the architecture information to be tested, the session history, and the structured target blueprint. The assessment prompt is a structured request object containing dedicated fields for inputting contextual information, the architecture data to be evaluated, the assessment dimensions, and task instructions. The task instructions explicitly instruct the audit language model to perform specific tasks. For example, the model may be instructed to analyze the "architecture information to be tested" and output all potential risk points and conflict indicators based on the constraints and performance benchmarks in the "structured target blueprint." The task instructions also require the audit language model to return results in a machine-readable structured text format, such as a text object containing fields for "risk points," "conflict indicators," and "suggestions." The system then matches the parameters of the architecture to be tested with the performance benchmarks and constraints in the knowledge base to generate a comparison result. The audit language model is then invoked to process the assessment prompt and comparison result. Specifically, the audit language model utilizes a general large language model and, through system prompts, guides it to act as an architecture audit expert. The audit language model outputs its analysis results in a structured text format containing fields for conflict indicators, detailed descriptions, and severity levels.

[0037] Based on the audit language model, an initial comprehensive assessment result is generated, including conflict indicators. For example, the conflict indicator might be "Conflict indicator-001: The estimated cost of the current architecture is detected to be 6,000 yuan, exceeding the budget cap of 3,000 yuan." If a conflict indicator is present in the initial comprehensive assessment result, the conflict domain and conflict type are determined by parsing the conflict indicator. Subsequently, the solution evaluation module invokes the deployment policy rule set and, based on the conflict type, dynamically calculates the adjudication weight for the conflict indicator. Specifically, the system first assigns base weights to the conflict domains based on a preset priority list within the deployment policy rule set. For example, "security compliance" is set to 100 points, and "cost" is set to 70 points (with a weight range of 0-100). Next, the system determines an adjustment coefficient by analyzing terminal configuration preferences extracted from session history information. Based on the terminal configuration preferences, the adjustment multiplier is applied to the base weight. For example, the conflict domain of "Conflict Identifier-001" is "cost", and e-commerce company A did not emphasize cost as the highest priority in the session history information. Therefore, the dynamically calculated arbitration weight of this conflict is 70; if the user has stated in the interaction that "cost is the primary consideration", the preset 1.2x adjustment multiplier is applied to the basic weight.

[0038] During the dynamic calculation of the adjudication weights for conflict indicators, preliminary adjustment actions for the conflict indicators are generated based on the component templates and constraint rules in the architectural knowledge base. Based on the preliminary adjustment actions and the architectural knowledge base, the dependencies of the components related to the preliminary adjustment actions are traversed. Based on these dependencies, the scope of the preliminary adjustment actions' dependencies on the architectural components is quantified to generate a chain reaction index. The chain reaction index is then used as an independent adjustment factor and positively added to the dynamic calculation of the adjudication weights to generate the adjudication weights. By proactively assessing and quantifying the potential risks of solutions, risk-driven prioritization is achieved, improving collaborative efficiency and decision-making quality.

[0039] Based on the arbitration weight, the conflict identifiers are sorted and the arbitration priority is generated. Based on the arbitration priority, a clarification inquiry containing a recommended solution is generated, such as "Analysis found that the current solution cost exceeds the budget; according to the rules, high availability is the highest priority. It is recommended to adjust the computing instance to an automatic scaling group of 2 to 6 instances to optimize costs while meeting availability." and ask whether to adopt this suggestion. Finally, after receiving feedback information indicating "adoption", the solution evaluation module updates the session history information and modifies the structured target blueprint based on the feedback information, triggering a new round of evaluation, and finally generating a comprehensive evaluation result without conflict identifiers. By establishing a closed-loop evaluation process, the static solution review is transformed into a dynamic collaborative optimization process, which realizes in-depth risk diagnosis and dynamic correction of architecture deployment, ensuring the rationality of the final solution.

[0040] Furthermore, the strategy language model is used to identify the multi-objective trade-offs between the comprehensive evaluation results and the structured goal blueprint, and a candidate strategy matrix is ​​generated by combining the multi-objective trade-offs with the preset strategy axis. The candidate strategy matrix is ​​then quantitatively generated through predictive reasoning to generate a risk-adjusted utility score. Based on the risk-adjusted utility score, an optimal execution parameter set is output. The corresponding system deployment decision module specifically includes: See Figure 3The deployment decision module uses a policy language model to semantically correlate and pattern-match the comprehensive evaluation results with the structured target blueprint, identifying multi-objective trade-offs. For example, a multi-objective trade-off might be "How to configure an autoscaling group to minimize monthly costs, under 3,000 yuan, while meeting 2,000 QPS peak performance?" Based on these multi-objective trade-offs, the system deconstructs relevant best practices for infrastructure components and adaptively integrates these components to generate a customized baseline architecture solution. The deployment decision module combines pre-defined policy axes, such as "cost first" and "performance first," and performs a multi-dimensional transformation on the baseline architecture solution, ultimately generating candidate architecture adjustment solutions with different design priorities but all possessing technical feasibility. A candidate policy matrix is ​​then constructed based on these candidate architecture adjustment solutions. For example, Solution A (cost first): minimum number of instances: 2, maximum number of instances: 6; Solution B (performance first): minimum number of instances: 3, maximum number of instances: 6.

[0041] In the process of generating the candidate policy matrix, an initial Pareto-optimal solution set is constructed based on the initial candidate architecture adjustment schemes, and an iterative optimization search is performed. This iterative optimization search includes: selecting solutions adjacent to the target dimension as parent solutions based on the current Pareto-optimal solution set; generating child candidate architecture solutions by weighted averaging the execution parameters of the parent solutions; evaluating the child candidate architecture solutions using the policy language model to output evaluation data, performing Pareto-optimal screening based on the evaluation data, and updating the Pareto-optimal solution set until convergence; the converged Pareto-optimal solution set is used as the final candidate architecture solution, and a candidate policy matrix is ​​constructed based on the final candidate architecture solution. By expanding the decision-making basis from a limited number of alternatives to a Pareto-optimal solution set closer to the true optimal boundary, the probability of finding a more globally optimal solution is increased, thereby improving the decision-making quality and deployment efficiency of the system.

[0042] Then, the system's built-in performance and cost evaluation engine is called to perform predictive reasoning on the candidate architecture adjustment plan. The predictive reasoning process is as follows: based on performance benchmark data, a preset linear interpolation method is used to estimate the response delay of the architecture under test under peak load; the real-time pricing API interface of the target cloud platform is called, and the cost indicators are accurately estimated in combination with the resource usage time in the plan; the response delay cost indicators are integrated into an estimated data report; the estimated data report is integrated and interpreted using a policy language model to generate an estimated evaluation report that includes performance, cost, and reliability dimensions. For example: Plan A report: Estimated monthly cost: 2,850 yuan. Under a peak load of 2,000 queries per second, the 99th percentile response latency is 180 milliseconds, meeting the requirements. Plan B report: Estimated monthly cost is 3,400 yuan. Under a peak load of 2,000 QPS, the 99th percentile of response latency is 120ms, which is better performance.

[0043] Finally, based on the user's cost constraint of "keeping costs within 3,000 yuan," the system dynamically constructs a utility function and calculates the risk-adjusted utility scores of the candidate solutions. For example, Solution A, because it fully meets all constraints, has a risk-adjusted utility score of 92; Solution B, because it exceeds the budget constraint, has a risk-adjusted utility score of 0. This utility function is a multi-factor scoring model that specifically involves comparing the predicted values ​​of the candidate solutions with the cost, performance, and reliability dimensions in the structured target blueprint and linearly normalizing them to generate a satisfaction score ranging from 0 to 100. Secondly, importance weights are assigned to the evaluation dimensions based on the terminal configuration preferences reflected in the session history information. Specifically, the system reuses the arbitration weights calculated for each conflict domain and normalizes them as the importance weights for each evaluation dimension. The weighted sum of the satisfaction scores for each dimension is then used to determine the base utility score. The multi-factor scoring model also includes a penalty mechanism. For candidate architectural adjustment plans that violate hard constraints, their risk-adjusted utility scores are directly determined as preset penalty values, for example, the risk-adjusted utility scores are set to zero. For compliant candidate architectural adjustment plans, their basic utility scores are directly used as risk-adjusted utility scores. For example, Plan B exceeds the budget cap of 3,000 yuan, resulting in Plan B's utility score being directly set to 0, while the fully compliant Plan A retains its higher original utility score of 92.

[0044] The system makes a final ranking and decision based on risk-adjusted utility scores, selecting the solution with the highest utility score and outputting the final optimal execution parameter set, for example: {"Compute Component": {"Type: AWS Auto Scaling Group","Instance Type: t3.Medium","Minimum Number of Instances: 2","Maximum Number of Instances: 6"}, "Database Component": {"Type: Cross-AZ Database","Instance Specifications: t3.Medium","Encryption at Rest: True"}}. By generatively generating diverse candidate solutions and combining them with predictive reasoning for quantitative risk assessment, the decision-making process, which relies on qualitative comparison, is transformed into one that combines both creative and scientific approaches. This improves the probability of finding the global optimal solution and the credibility of the final result, while reducing reliance on human experience.

[0045] Furthermore, the generation of a deployment configuration file adapted to the target environment based on the optimal execution parameter set corresponds to a solution generation module of the system, specifically including: First, the optimal execution parameter set output by the decision unit is received, and at the same time, target environment information, such as Amazon Web Services (AWS), is obtained from the session history information stored in the information interaction module.

[0046] The solution generation module contains a generator plug-in library, which has pre-installed configuration file generators for a variety of mainstream target environments, such as AWS, Microsoft Azure, and Google Cloud Platform. The plug-ins in the generator plug-in library follow a predefined interface specification, which requires the plug-in to provide three core methods: the first method is used to declare the name of the target environment supported by the plug-in; the second is the core generation method, which is designed to receive the standardized optimal execution parameter set output by the core engine as the only input and return the complete configuration file content in string format that is specific to the target platform; the third is the verification method, which is used to check the syntactic validity of the configuration file content after generation and return the check results and possible error information. Based on the acquired target environment information, the deployment configuration file generator plug-in that follows the interface specification is automatically selected and loaded.

[0047] The deployment configuration file generator plug-in reads the input optimal execution parameter set and executes a mapping process including dependency resolution. The mapping process is as follows: first, the mapping logic within the plug-in converts the common parameters within the system into specific attributes in a specific configuration language according to preset rules, for example, converting the "minimum number of instances" into "min_size"; during the conversion process, the system generates code blocks for the resources to be created, identifies the dependencies between resources and uses temporary placeholders, for example, the application server's dependency on the database address. Subsequently, the plug-in generates a resource dependency graph based on the dependencies. The specific generation process includes: the plug-in first traverses the optimal execution parameter set and generates graph nodes for the resources to be created, for example, database instances and computing instances; then, the plug-in analyzes the dependencies between the nodes. When it is identified that a property of a downstream resource references the output of an upstream resource, for example, a computing instance references the address of a database instance, a directed edge from upstream to downstream is established between the corresponding nodes of the two resources, and a resource dependency graph is generated based on the directed edges of all dependencies. Finally, the system uses a topological sorting algorithm to traverse the resource dependency graph to ensure that resources without dependencies are written into the configuration first, and resources with dependencies are written later. When writing, the placeholders referenced by the resources are replaced with the actual output values ​​generated by the upstream resources.

[0048] In this way, the plug-in maps the optimal execution parameter set into the syntax code of a specific configuration language, which can be the HashiCorp Configuration Language (HCL). For example, the system's internal decision parameter "instance type" and its value are mapped to the attributes used to define specific instance specifications in the target configuration language; the parameter "minimum number of instances" is mapped to the attribute that defines the lower limit of scaling; and the Boolean value status of "static encryption" is mapped to the corresponding attribute for turning storage encryption on or off. Based on the converted configuration code, the final deployment configuration file is generated. At the same time, a grammatical validity check is performed on the deployment configuration file. For example, the verification tool that comes with the configuration language is called to ensure that the configuration file contains no grammatical errors and can be correctly parsed and executed by subsequent deployment tools. By mapping the optimal execution parameter set into the configuration code of the target environment and using the plug-in library to achieve cross-platform adaptation, the automatic generation of executable deployment files from intelligent decision-making is achieved, improving the efficiency and flexibility of architecture deployment.

[0049] Gather full-link process data, which includes: architecture deployment requirements, structured target blueprints, comprehensive evaluation results, and optimal execution parameter sets. Based on the preset explanation task prompt template, the collected full-link process data is structured and integrated to generate a comprehensive explanation prompt. The explanation task prompt template is a structured text instruction set, which specifically includes specific fields and step-by-step instructions for filling in data. The step-by-step instructions may include the following steps: Step 1. Please repeat the user's core business goals based on the content of the "User Requirements Original Text" field. Step 2. Based on the "Final Technology Blueprint" and "Optimal Execution Parameter Set", please identify and summarize the key potential risk points discovered during the solution evaluation process. Step 3. Please explain the key trade-off decisions made to resolve these risks or conflicts. Step 4. Please clearly demonstrate why the "adopted execution parameters" are the optimal choice that meets all constraints and trade-offs, and associate them with the user's business goals.

[0050] The large language model is called to process the comprehensive explanation prompts to generate structured natural language explanation text. For example, the natural language explanation text may include: Requirements Restatement and Confirmation: We have received your request to deploy a backend service architecture for an e-commerce platform. The core goal is to achieve 99.95% availability and 2000QPS peak performance on AWS, while strictly controlling the monthly cost within 3000 yuan. Summary of potential risks: Initial assessments revealed that using large server instances with fixed specifications would meet performance requirements, but would exceed budget and fail to achieve elastic high availability. Explanation of multi-objective trade-offs: To resolve the core conflict between availability and cost, the system made key trade-offs. After interactive confirmation with you, the "elastic auto-scaling" solution was adopted. This solution maximizes cost-effectiveness while meeting the rigid high availability requirements. Reasons for adopting the optimal execution parameter set: The final decision was to use medium-sized t3 instances configured as an auto-scaling group of 2-6 nodes. This was because it could meet peak performance of 2000 queries per second while keeping daily operating costs low. Elastic scaling was only required during peak traffic, keeping total costs within budget. By integrating data from across the entire process and generating a comprehensive argument including a risk summary, trade-offs, and rationale for adoption, the automated decision-making process was made transparent and traceable, improving the reliability of the final output solution.

[0051] By building a full-link intelligent process, the present invention transforms complex and vague architecture design requirements into specific and quantifiable automated processes, thereby improving the efficiency of architecture deployment, reducing the dependence of complex architecture design on human experience, and enhancing the quality and reliability of the final deployment solution.

[0052] Example 2: This embodiment applies an architecture intelligent deployment system based on a large language model to another scenario, in which e-commerce company A already has a preliminary design plan and needs to introduce an architecture intelligent deployment system to verify and optimize the design plan.

[0053] First, e-commerce company A uploads a structured requirements document defined with preset parameters as its architecture deployment requirements through the hybrid input interface of the information exchange module. This structured requirements document defines a solution using three fixed c5.large compute instances and one rds.m5.large instance. At the same time, e-commerce company A adds a natural language requirement: "Verify the feasibility of this solution, within a budget of 3,000 yuan, to meet 2,000 queries per second and 99.95% availability." After receiving this mix of structured and unstructured requirements, the requirements parsing module parses the natural language requirements and converts them into quantitative technical indicators. These quantitative technical indicators are then combined with the architecture deployment requirements to construct a structured target blueprint.

[0054] After receiving the architecture under test and the structured target blueprint, the solution evaluation module conducts a compliance audit. The system accesses the architecture knowledge base to obtain corresponding performance benchmarks and best practices. The audit language model then compares and analyzes the preliminary design against these benchmarks and best practices. For example, given that a c5.large instance costs approximately 1,800 yuan per month and the "High Availability Elastic E-commerce Backend Model" requires autoscaling, the module generates an initial comprehensive evaluation result with conflict indicators. For example, Conflict Indicator 201: The solution's estimated cost exceeds the 3,000 yuan budget constraint; Conflict Indicator 202: The solution with a fixed number of instances does not conform to the "High Availability Elastic E-commerce Backend Model" and is expected to waste over 70% of resources during non-promotional periods, failing to meet cost-effectiveness best practices. The solution evaluation module then dynamically calculates the adjudication weights for the conflict indicators. For example, the system might calculate a weight of 90 for "Conflict Indicator 201" and 75 for "Conflict Indicator 202." Based on the adjudication weights, the system prioritizes cost as the primary issue and constructs clarification inquiries accordingly, recommending an optimization solution based on "autoscaling groups" to e-commerce company A. By intelligently comparing and analyzing the preliminary design plans submitted by users with the best practices in the knowledge base, we can proactively identify potential cost and performance risks in the plans, thereby improving the cost-effectiveness and technical rationality of the architectural plans.

[0055] The system sends a clarification query containing the conflict indicator to e-commerce company A and recommends an optimization solution based on the "auto-scaling group." After e-commerce company A confirms the solution through interaction, the decision module is deployed to initiate the decision-making process: First, the module identifies "optimizing costs while meeting performance and availability" as the current core multi-objective trade-off. Next, it generates a candidate strategy matrix containing a variety of different scaling configurations, combining preset strategy axes. The decision-making unit then performs predictive reasoning on the candidate solutions in the candidate strategy matrix, deducing their estimated costs and response delays under different traffic conditions, and ultimately calculating their respective risk-adjusted utility scores. The system selects the solution with the highest utility score as the optimal execution parameter set and passes it to the solution generation module, ultimately outputting an optimized deployment configuration file and a natural language explanation. The natural language explanation contains information comparing the advantages and disadvantages of the initial design solution with the system-optimized solution.

[0056] Example 3: This embodiment applies an architecture intelligent deployment system based on a large language model to the e-commerce company A described in Example 1. In order to reduce operating costs, e-commerce company A inputs a new request, the content of which is to disable the static encryption function at the database level in order to minimize costs.

[0057] Based on the request, the requirements parsing module determines the terminal configuration preference of "prioritizing cost over security." During its evaluation, the solution evaluation module retrieves the relevant constraint rule from the architecture knowledge base: "Rule ID-SEC01: For e-commerce architectures involving payment functionality, database components must have static encryption enabled."

[0058] E-commerce company A's request to disable static encryption conflicts with rule ID-SEC01. The solution evaluation module generates a conflict indicator in the initial comprehensive evaluation results: "Conflict indicator 301: Request to disable static encryption conflicts with rule ID-SEC01." After identifying the conflict indicator, the solution evaluation module immediately initiates the adjudication process. First, the conflict domain and type are analyzed. For example, the domain might be "security" and the type might be "hard constraint violation." The deployment policy rule set is then invoked to dynamically calculate the adjudication weight for the conflict indicator. Because this conflict is a "hard conflict" under the "security compliance" domain, its adjudication weight is calculated as a numerical value representing the highest priority. The adjudication priority generated based on this weight is then assigned the highest level. Based on the adjudication priority, the system interrupts its normal optimization and decision-making processes. By identifying hard conflicts with core constraints and immediately invoking pre-set rules for adjudication, invalid or dangerous user requests can be quickly interrupted and handled, improving the robustness of the system's decision-making.

[0059] The solution generation module is invoked to generate a natural language explanation of the mandatory ruling. Ultimately, the system outputs a denial response to e-commerce company A through the information exchange module: "Request denied; the requested action is in conflict with security compliance (Rule ID-SEC01). Through dynamic calculation, the system determines that the conflict has the highest, non-negotiable priority. To ensure payment security, the database's static encryption function is a hard, unchangeable constraint." By generating a denial response that references the specific ruling rule, a clear explanation of the mandatory decision is achieved, ensuring that users understand the compliance rationale behind it and enhancing system transparency.

[0060] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An architecture intelligent deployment system based on a large language model, characterized by: include: Information interaction module, which obtains architecture deployment requirements and stores the preset architecture knowledge base and deployment strategy rule set; The requirement parsing module identifies architecture deployment requirements and extracts deployment intent primitives. Through the preset mapping relationship and architecture knowledge base, it parses the deployment intent primitives to generate a structured target blueprint. The solution evaluation module uses the audit language model to evaluate the structured goal blueprint and identify conflict indicators; The deployment strategy rule set is called to dynamically calculate the adjudication weight of the conflict identifier, and the adjudication priority is generated using the adjudication weight; a clarification inquiry is generated based on the adjudication priority to obtain feedback information, and the structured target blueprint is modified based on the feedback information, triggering the re-evaluation of the audit language model, and iteratively generating a comprehensive evaluation result; The deployment decision module uses the policy language model to identify the multi-objective trade-offs between the comprehensive evaluation results and the structured goal blueprint, and combines the multi-objective trade-offs with the preset policy axes to generate a candidate policy matrix. By predictively reasoning the candidate strategy matrix, a risk-adjusted utility score is quantitatively generated, and an optimal execution parameter set is output based on the risk-adjusted utility score; The solution generation module outputs the architecture deployment solution based on the optimal execution parameter set.

2. The architecture intelligent deployment system based on a large language model according to claim 1, characterized in that: The information interaction module includes: the architecture knowledge base includes best practice patterns, component templates, performance benchmarks and constraint rules; the deployment strategy rule set includes the predefined sorting rules for the decision priority.

3. The architecture intelligent deployment system based on a large language model according to claim 2, characterized in that: The demand parsing module includes a natural language processing unit and a preset mapping database; the natural language processing unit uses natural language processing technology to identify and extract deployment intention primitives including performance, cost and security based on the architecture deployment requirements; A preset mapping database stores the mapping relationship between the deployment intention primitives and quantitative technical indicators, where the quantitative technical indicators include target response delay and budget upper limit. The deployment intention primitives are converted into quantitative technical indicators based on the mapping relationship. Based on the architecture knowledge base, constraint rules related to the quantitative technical indicators are retrieved, and a structured target blueprint containing constraint boundaries is constructed by combining the constraint rules and technical indicators.

4. The architecture intelligent deployment system based on a large language model according to claim 3, characterized in that: The solution evaluation module includes: dynamically constructing a test architecture based on the structured target blueprint and the component template; calling the audit language model to compare and analyze the test architecture with the performance benchmark and constraint rules, and generating an initial comprehensive evaluation result including potential risk points and conflict identifiers, where the conflict identifier is used to indicate that there is a logical conflict between the test architecture and the constraint rules; when there is a conflict identifier in the initial comprehensive evaluation result, parsing the conflict identifier to obtain the conflict domain and conflict type of the conflict identifier, calling the deployment strategy rule set in combination with the conflict type, dynamically calculating the ruling weight of the conflict identifier, sorting the conflict identifier based on the ruling weight, and generating a ruling priority; based on the ruling priority, constructing and issuing a clarification inquiry containing a recommended solution to obtain feedback information; based on the feedback information, correcting the parameters of the structured target blueprint; and re-triggering the audit language model for re-evaluation based on the corrected structured target blueprint until a comprehensive evaluation result is generated in which no conflict identifier is present.

5. The architecture intelligent deployment system based on a large language model according to claim 4, characterized in that: The deployment decision module includes: using a policy language model to semantically associate and pattern match the comprehensive evaluation results with the structured target blueprint to locate multi-objective trade-off points; based on the multi-objective trade-off points, deconstructing best practice patterns as infrastructure components; generating a baseline architecture solution by adaptively integrating the infrastructure components, generating candidate architecture adjustment solutions based on the multi-dimensional conversion of the baseline architecture solution based on a preset policy axis, and constructing a candidate policy matrix based on the candidate architecture adjustment solutions; performing predictive reasoning on the candidate policy matrix and generating an estimated evaluation report; calculating the risk-adjusted utility scores of the candidate architecture adjustment solutions based on the estimated evaluation report, ranking and deciding based on the risk-adjusted utility scores, and outputting the optimal execution parameter set.

6. The architecture intelligent deployment system based on a large language model according to claim 1, characterized in that: The solution generation module includes: using a preset generator plug-in library to map the optimal execution parameter set into an architecture configuration code, generating a deployment configuration file based on the architecture configuration code; generating a natural language argument based on full-link data; and outputting an architecture deployment solution including the deployment configuration file and the natural language argument.

7. The architecture intelligent deployment system based on a large language model according to claim 6, characterized in that: The natural language argument includes: integrating full-link data including architecture deployment requirements, structured target blueprints, comprehensive evaluation results and optimal execution parameter sets; reasoning the full-link data based on a preset explanation task prompt template to generate a natural language argument including a summary of potential risk points, a description of multi-objective trade-offs and reasons for adopting the optimal execution parameter set.

8. A method for intelligent architecture deployment based on a large language model, characterized in that: include: Obtain architecture deployment requirements, store preset architecture knowledge base and deployment strategy rule sets; Identify architecture deployment requirements and extract deployment intent primitives. Analyze deployment intent primitives through preset mapping relationships and architecture knowledge base to generate a structured target blueprint. Use the audit language model to evaluate the structured goal blueprint and identify conflict indicators; The deployment strategy rule set is called to dynamically calculate the adjudication weight of the conflict identifier, and the adjudication priority is generated using the adjudication weight; a clarification inquiry is generated based on the adjudication priority to obtain feedback information, and the structured target blueprint is modified based on the feedback information, triggering the re-evaluation of the audit language model, and iteratively generating a comprehensive evaluation result; The strategy language model is used to identify the multi-objective trade-offs between the comprehensive evaluation results and the structured goal blueprint, and a candidate strategy matrix is ​​generated by combining the multi-objective trade-offs with the preset strategy axes. By predictively reasoning the candidate strategy matrix, a risk-adjusted utility score is quantitatively generated, and an optimal execution parameter set is output based on the risk-adjusted utility score; Output architecture deployment plan based on the optimal execution parameter set.

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