Intelligent cloud workflow generation management platform fusing large language model

Abstract

The application relates to the technical field of cloud native software engineering, in particular to an intelligent cloud workflow generation and management platform fusing a large language model, which comprises: a working condition modeling module, which is used for automatically constructing a system specification model containing formal design specifications and expected performance targets based on inventory code, real-time traffic and telemetry data; an adversarial refinement module, which is used for realizing automatic generation, verification and correction of candidate workflow code through an adversarial iterative cycle of a generative language model and a verification engine until no new counterexample is generated; and a deployment decision module, which is used for performing quantitative risk assessment based on iteration history and executing automatic deployment decision and authorization. The application solves the problems of dependence on manual work, low efficiency and unstable quality in a traditional development process, and realizes automation, quantification and high reliability of the whole process of workflow generation and management.

Description

Technical Field

[0001] This invention relates to the field of cloud-native software engineering technology, specifically to an intelligent cloud workflow generation and management platform that integrates large language models. Background Technology

[0002] The creation and management of cloud workflows is a core software engineering activity. The software engineering lifecycle typically involves requirements analysis, design, source code generation, and design verification.

[0003] In existing technologies, AIOps platforms have used machine learning models (based on historical data) to predict business load and execute pre-written scalable workflows, which performs well when dealing with predictable business patterns; meanwhile, large language models (LLMs) have allowed platforms to dynamically generate source code based on natural language intent.

[0004] However, in practical software engineering applications, when customers introduce entirely new business models (such as the instantaneous traffic model of live-streaming e-commerce), the predictive models of AIOps platforms may fail due to model drift and make self-misjudgments, such as misidentifying real users as DDoS attackers, thus triggering P0-level (highest level) incidents. In this situation, the System Engineer (SRE) intends to redesign and generate a V2 workflow capable of handling the new business logic of live-streaming e-commerce.

[0005] The shortcoming of existing technologies lies in the absence of a design verification step in software engineering. When the large language model dynamically generates (or designs) this entirely new V2 workflow, there is no mechanism in the platform that can automatically, in real time, and securely (before execution) provide the SRE with a software design simulation or execution plan.

[0006] In a P0-level incident, the SRE is unable to preview or simulate how the newly designed AI-powered V2 workflow will handle the instantaneous traffic of live-streaming e-commerce before execution. The SRE cannot confirm whether the new AI design (V2) has truly solved model drift or will (like V1) misjudge again.

[0007] Executing a completely new workflow generated by AI without software design simulation carries the risk of catastrophic secondary crashes. This completely breaks the "design-generate" closed loop of software engineering in crisis scenarios.

[0008] To address this, the present invention proposes an intelligent cloud workflow generation and management platform that integrates a large language model. Summary of the Invention

[0009] The purpose of this invention is to provide an intelligent cloud workflow generation and management platform that integrates large language models, and achieves pre-deployment design verification through AI adversarial refinement.

[0010] To achieve the above objectives, the present invention provides the following technical solution:

[0011] An intelligent cloud workflow generation and management platform integrating large language models includes:

[0012] Operating condition modeling module: Collects existing workflow code, real-time user request traffic characteristic data, and cloud infrastructure monitoring telemetry data; Based on existing workflow code, real-time user request traffic characteristic data, and cloud infrastructure monitoring telemetry data, constructs a system specification model that includes formal design specifications and expected performance targets;

[0013] The adversarial refining module executes an iterative loop, generating candidate workflow code based on the system specification model and counterexamples using a generative language model. It then generates and executes reproducible test vectors based on formal design specifications and expected performance goals using a verification and testing engine. These test vectors are used to verify the specification compliance and performance of the candidate workflow code under simulated conditions, and new counterexamples are recorded. The loop terminates when no new counterexamples are generated for a preset number of consecutive rounds, and when the number of iterations reaches a preset threshold, and the final deployment candidate is output.

[0014] Deployment Decision Module: Quantitatively analyzes all recorded counterexamples and performance deviations, generates a deployment risk assessment report; executes decisions based on risk thresholds, and when the risk level is lower than a preset threshold, outputs an authorization instruction to initiate the production deployment process of candidate workflow code.

[0015] Preferably, the working condition modeling module specifically includes:

[0016] The operational condition modeling module responds to the workflow identifier of the operation and maintenance instructions, and pulls the existing workflow code of the current production environment through the API interface; it collects real-time user request traffic characteristic data related to the workflow within a preset time window, including request rate timing, response latency, and request type distribution; it collects cloud infrastructure monitoring telemetry data, including computing resource utilization, memory consumption, and network throughput; it integrates the existing workflow code, real-time user request traffic characteristic data, cloud infrastructure monitoring telemetry data, and operation and maintenance instructions, and uses natural language processing technology to parse the operation and maintenance instructions to extract formalized business objectives; it scans the abstract syntax tree of the existing workflow code through static code analysis to extract the rigid constraints hard-coded in the code as engineering constraints; it transforms the real-time user request traffic characteristic data and cloud infrastructure monitoring telemetry data into a quantitative load model and resource baseline through feature engineering; and it integrates the formalized business objectives, engineering constraints, and quantitative load model and resource baseline into a unified, machine-readable structured system specification model.

[0017] Preferably, in the iterative loop of the adversarial refining module, the specific task of the generative language model is:

[0018] The system receives the system specification model and counterexamples from the previous iteration. If it is the first iteration, there are no counterexamples. A deployment candidate body is generated, which includes candidate workflow code, code implementation annotations, and a confidence score. The candidate workflow code is newly generated business logic code that satisfies the formal design specifications and expected performance goals defined in the system specification model, complies with the engineering constraints contained in the system specification model, and fixes all known counterexamples. The code implementation annotations are natural language descriptions generated by the generative language model for the generated business logic code fragments, used to explain how they satisfy the formal design specifications and expected performance goals. The confidence score is the generative language model's self-evaluation score for the generated code in terms of satisfying the specifications and performance goals.

[0019] Preferably, the adversarial refining module includes a collaborative verification and testing engine and an auditing language model:

[0020] The verification and testing engine is responsible for performing formal verification, specifically including: generating logical test vectors using a model checker for formal design specifications, and generating stress test vectors using an automated load testing framework for expected performance targets; the auditing language model is a classification and generation dual-task model based on a Transformer encoder-decoder architecture, responsible for performing semantic adversarial auditing, specifically including: reading the system specification model and the code implementation annotations and confidence scores generated by the generative language model, and generating semantic test scenarios containing potential logical vulnerabilities to challenge the annotations and scores; the verification and testing engine also compiles the semantic test scenarios into executable test vectors, and merges them with the logical test vectors and stress test vectors to output a comprehensive adversarial test vector.

[0021] Preferably, the specific implementation steps of the verification and iterative correction loop in the adversarial refining module are as follows:

[0022] An isolated, logically consistent simulation environment is automatically constructed, with resource configuration based on the resource baseline in the system specification model. Candidate workflow code is deployed in the environment, with the load model from the system specification model injected as background traffic, and comprehensive adversarial test vectors injected as attack traffic. A monitoring observer is deployed to compare the actual operating metrics of the code with the specifications and objectives in the system specification model in real time. When behavioral violations and performance deviations triggered by formal verification are detected, and failures triggered by business logic adversarial auditing where the expected business logic does not match the actual behavior are detected, the violations are recorded as a new structured counterexample. The new counterexample is fed back to the audit language model, which converts the structured new counterexample into a natural language description that the generative language model can understand for repair. The natural language description serves as a new constraint to initiate the next iteration.

[0023] Preferably, after the adversarial refining module terminates the loop and outputs the final deployment candidate, the decision-making logic of the deployment decision module is specifically used for:

[0024] The process involves quantitatively analyzing the iteration history, examining all counterexamples and performance deviations recorded in the adversarial loop, and calculating risk indicators. These indicators include: formal test coverage, business logic scenario coverage, the source distribution of final counterexamples, the failure rate in high-confidence regions, and the final performance deviation compared to the expected performance target. A deployment risk assessment report is generated, calculating a comprehensive risk score based on the risk indicators and producing an assessment report with natural language explanations. A decision is then made based on a business acceptance risk threshold, comparing the comprehensive risk score with a preset business acceptance risk threshold. If the comprehensive risk score is lower than the business acceptance risk threshold, an authorization command is output and an encrypted authorization token is automatically generated, serving as the credential for initiating the downstream automated deployment process. If the comprehensive risk score is not lower than the business acceptance risk threshold, the deployment is automatically rejected, and an alarm message is output to the operations engineer.

[0025] Preferably, when the deployment decision module outputs the authorization command, it performs an additional task: extracting local state invariants from the formal design specifications in the final deployment candidate, and combining them with the risk indicators in the deployment risk assessment report, mapping and compiling the local state invariants into a set of production environment runtime security observers; this set of security observers is attached to the candidate workflow code and output along with the authorization command; the runtime security observers are used to capture corresponding violation events when the deployed workflow triggers behavior that violates the formal design specifications in the production environment, and when the performance of the deployed workflow touches the risk area indicated in the risk indicators; the runtime security observers format the captured violation events into new production environment structured counterexamples and automatically send the production environment structured counterexamples back to the adversarial refining module; the audit language model receives the production environment structured counterexamples, analyzes the root cause, and elevates them to high-priority business logic test scenarios that need to be executed in all future iterations, triggering a new iterative optimization loop based on the production environment structured counterexamples.

[0026] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0027] 1. This invention enables System Engineers (SREs) to safely and fully pre-deploy the new AI-generated workflow under simulated instantaneous traffic by using a high-fidelity simulation environment and adversarial test vectors in the adversarial refinement module, thus eliminating the verification blind spots of existing technologies.

[0028] 2. This invention, through the deployment decision module, performs quantitative analysis of the iteration history and generates a risk assessment report before deployment, ensuring that only candidates that pass multiple rounds of adversarial and risk convergence can obtain deployment authorization, fundamentally preventing defective AI designs from entering the production environment.

[0029] 3. This invention not only connects "design-generation-verification" through "condition modeling-adversarial refinement-deployment decision", but also automatically feeds back the actual violations in the production environment to the modeling module through a runtime security observer, forming a complete and self-optimizing AIOps engineering closed loop. Attached Figure Description

[0030] Figure 1 This is a module diagram of an intelligent cloud workflow generation and management platform that integrates a large language model according to the present invention.

[0031] Figure 2 This is a flowchart of an intelligent cloud workflow generation and management method that integrates a large language model according to the present invention.

[0032] Figure 3 This is a schematic diagram of the internal iterative loop of the anti-refining module in an embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Other embodiments obtained by those skilled in the art based on the ideas in this specification without creative effort all fall within the protection scope of this invention.

[0034] Reference Figures 1 to 3 This invention provides an intelligent cloud workflow generation and management platform that integrates large language models, and the specific technical solution is as follows.

[0035] An intelligent cloud workflow generation and management platform integrating large language models, referring to Figure 1 , Figure 2 , Figure 1 A module diagram of an intelligent cloud workflow generation and management platform integrating a large language model. Figure 2 This is a flowchart illustrating an intelligent cloud workflow generation and management method that integrates large language models, specifically including:

[0036] Operating condition modeling module: Collects existing workflow code, real-time user request traffic characteristic data, and cloud infrastructure monitoring telemetry data; Based on existing workflow code, real-time user request traffic characteristic data, and cloud infrastructure monitoring telemetry data, constructs a system specification model that includes formal design specifications and expected performance targets;

[0037] The adversarial refining module executes an iterative loop, generating candidate workflow code based on the system specification model and counterexamples using a generative language model. It then generates and executes reproducible test vectors based on formal design specifications and expected performance goals using a verification and testing engine. These test vectors are used to verify the specification compliance and performance of the candidate workflow code under simulated conditions, and new counterexamples are recorded. The loop terminates when no new counterexamples are generated for a preset number of consecutive rounds, and when the number of iterations reaches a preset threshold, and the final deployment candidate is output.

[0038] Deployment Decision Module: Quantitatively analyzes all recorded counterexamples and performance deviations, generates a deployment risk assessment report; executes decisions based on risk thresholds, and when the risk level is lower than a preset threshold, outputs an authorization instruction to initiate the production deployment process of candidate workflow code.

[0039] Example 1:

[0040] This embodiment provides an intelligent cloud workflow generation and management platform that integrates a large language model. In this embodiment, the automated update and deployment of an order processing workflow on a typical e-commerce platform is used as an example. This e-commerce platform plans to upgrade its existing order processing workflow before a large-scale annual promotional event, aiming to integrate a new logic for redeeming coupons for a flash sale. The core technical challenge of this update lies in the fact that the new workflow code must meet stringent low-latency response performance targets while ensuring absolute consistency and correctness of data processing, even under the anticipated surge of tens of thousands of transactions per second during the promotional event. This embodiment will elaborate on how to apply the described method to intelligently generate, verify, and deploy this order processing workflow.

[0041] Furthermore, the operational condition modeling module specifically includes: a workflow identifier responding to maintenance instructions; pulling existing workflow code from the current production environment via an API interface; real-time collection of workflow-related real-time user request traffic characteristic data within a preset time window, including request rate timing, response latency, and request type distribution; collection of cloud infrastructure monitoring telemetry data, including computing resource utilization, memory consumption, and network throughput; fusion of the existing workflow code, real-time user request traffic characteristic data, cloud infrastructure monitoring telemetry data, and maintenance instructions; parsing the maintenance instructions using natural language processing technology to extract formalized business objectives; and scanning the source code abstract syntax tree (AST) of the existing workflow and the project's supporting configuration files (such as application.yaml, Kubernetes...) using static code analysis tools. The system (ConfigMaps) not only extracts hard-coded constants from the code, but also parses resource quotas and parameter settings in the configuration files, merging them into rigid constraints as engineering constraints; through feature engineering, it transforms real-time user request traffic characteristic data and cloud infrastructure monitoring telemetry data into quantitative load models and resource baselines; and integrates formalized business objectives, engineering constraints, and quantitative load models and resource baselines into a unified, machine-readable structured system specification model.

[0042] Specifically, the operational modeling module is activated when an operations engineer issues an update command containing a specific workflow identifier, such as `order-processing-workflow-v2.1`. Based on this identifier, the module calls the version control system's API to retrieve the corresponding workflow source code. Simultaneously, the module collects user request traffic characteristic data within a preset time window from the application performance monitoring system. This "preset time window" is a configurable parameter set by operations experts based on business cycles (such as historical promotional activities), for example, set to "every day from 2 PM to 3 PM within the past 30 days". The collected data includes request rate timings, P99 response latency, etc. The module also collects infrastructure telemetry data within the corresponding time window from the cloud platform monitoring components.

[0043] After data collection is complete, the module enters the information fusion and modeling phase. A natural language processing model specifically built for parsing operation and maintenance instructions is responsible for converting the instruction text into structured data. The model is built as follows: First, a pre-trained language model based on the Transformer encoder-decoder architecture is selected as the foundation; second, a proprietary dataset containing thousands of real operation and maintenance instructions and their corresponding JSON format tags is prepared for fine-tuning. For example, the instruction text "Peak concurrency capability needs to be increased to 30,000 QPS" corresponds to the tag {"performance_goals": {"qps": 30000}}; finally, sequence labeling is used to fine-tune the model, training it to recognize key entities (such as "QPS" and "latency") and their values ​​in the instructions.

[0044] In parallel, a static code analysis tool based on abstract syntax tree (AST) analysis scans the existing code. This tool traverses the AST of the code and not only identifies business state annotations used to establish variable mapping relationships (such as the custom annotation @StateVar), but also extracts hard-coded engineering constraints and key state variable indexes by matching specific function call signatures (such as database.pool.setMaxConnections(100)) and import statements of dependent libraries.

[0045] The feature engineering step processes the collected real-time data. The system uses the Seasonal-Trend Decomposition (STL) algorithm to process the request rate time series data. The STL algorithm is chosen because it is robust to outliers and can effectively decompose the time series into three components: trend, seasonality, and residual. The seasonality window parameter of the algorithm is preset to 24 hours based on business knowledge to capture daily periodicity. After decomposition, the trend component is used to fit the basic load function, the seasonal component is used to define the periodic impulse function, and the statistical distribution of the residual component is used to construct a probability distribution model describing random burst traffic. These parameters together constitute the quantified load model. At the same time, the baseline level of resource occupancy is calculated based on telemetry data.

[0046] All independently extracted and generated information fragments are integrated into a unified structured system specification model through a template slot-filling mechanism. The system pre-configures a TLA+ formal specification template library, with each template corresponding to a typical distributed system pattern (such as a strongly consistent state machine or an eventually consistent queue). This library uses JSON Schema, and each template contains three parts: an "applicable intent label," a "TLA+ code skeleton," and a "parameter mapping dictionary." The code skeleton reserves parameter slots marked with double curly braces (e.g., {{MAX_CONNECTIONS}}). The intent entities extracted from the NLP model's parsing of operational instructions are used to index and select the corresponding TLA+ template; the parameter mapping dictionary defines the correspondence between keys extracted from static analysis and template slots. The system executes a key-value pair matching algorithm to accurately fill the reserved variable slots in the template with parameter values ​​(e.g., 100) extracted from static code analysis. If the static analysis fails to extract a required parameter, the system will trigger a default value filling strategy or query the user. The processed data is ultimately archived in YAML structured format. If the soft objectives extracted by NLP (such as TPS>5000) and the static hard constraints (IO model = BIO) are logically mutually exclusive during the template parameter validation phase, the conflict resolution mechanism will automatically mark the contradiction and classify it as a major risk constraint. The processed data is ultimately archived in YAML structured format, and the system uses the lightweight YAML format to achieve model persistence. The model is stored in YAML format, and its schema contains four root nodes: the "IntentSpec" node stores the formal business objective key-value pairs extracted by NLP. These key-value pairs follow predefined domain-specific language specifications, including performance indicator keys (such as p99_latency, max_throughput) and business constraint keys (such as consistency_level), ensuring that the subsequent validation engine can perform accurate numerical comparisons; the "ResourceCon ...); the "ResourceCon" node stores the formal The "straints" node stores hard-coded engineering constraints extracted from static code analysis (such as max_db_connections: 100), serving as the physical boundary conditions for the generative language model. The "LogicConstraints" node encapsulates state machine code snippets described by TLA+ formal language, focusing on the system's initial state and state transition formulas. This snippet is marked as a strongly constrained context for the generative language model, forcing the generated code to satisfy the invariants defined in the formulas. The "LoadProfile" node stores traffic characteristics, specifically consisting of two parts: first, a time-series array of request rates based on timestamps, used to replay historical traffic trends; and second, fitted probability distribution parameters (such as Pareto distribution α values), used to generate randomized stress test background traffic in the simulation environment. This model serves as the sole input to the subsequent adversarial refinement module.

[0047] In a preferred embodiment, the working condition modeling module is further configured to: access an external event knowledge base containing future business activity plans; identify high-impact events related to the workflow that may lead to extreme working conditions in the future; use a prediction model to generate future working condition scenarios that have never appeared in historical data and contain hypothetical load models and forward-looking performance targets based on the characteristics of the high-impact events; and use the future working condition scenarios as an enhancement to the system specification model.

[0048] Specifically, after constructing the system specification model based on historical data, the operational condition modeling module accessed the company's marketing calendar via API, serving as an external event knowledge base. The module identified an upcoming high-impact event marked as the "Annual Mega Sale." A prediction model based on the Prophet time series forecasting algorithm, combined with event regression factors, was invoked. This model generated a hypothetical "mega sale" load model based on characteristics such as the historical mega sale growth rate (e.g., traffic doubling annually) and the marketing intensity of this event (e.g., a 50% increase in coupon issuance). This model predicted that within the first hour of the mega sale, the rate of order creation requests would reach 10 times the historical peak, and the request type distribution would become {'create_order': 95%, 'query_status': 4%, 'cancel_order': 1%}. Simultaneously, the module set a more stringent forward-looking performance target: under this extreme load, the P99 latency must still be maintained below 200ms. This future operating scenario, which includes hypothetical loads and forward-looking objectives, was added to the original system specification model file as a separate, high-priority test specification.

[0049] This solution elevates operational condition modeling from reactive to predictive, enabling the platform to "rehearse" for foreseeable but never-before-seen extreme business shocks. This ensures that the generated workflows not only cope with historical operational conditions but also possess the foresight to address future critical business challenges.

[0050] This solution constructs a comprehensive and accurate system specification model through automated data collection and multi-dimensional information fusion. Compared to traditional methods that rely on manual analysis and experience-based judgment, this solution ensures that the foundational data for subsequent workflow generation and verification is quantified, objective, and complete, avoiding development risks caused by misunderstandings of requirements or inaccurate assessments of operating conditions.

[0051] Furthermore, referring to Figure 3In the iterative loop of the adversarial refinement module, the specific task of the generative language model is to: receive the system specification model and the counterexamples recorded in the previous iteration (if it is the first iteration, the counterexamples are empty); generate a deployment candidate body containing candidate workflow code, code implementation annotations, and confidence scores; the candidate workflow code is newly generated business logic code that satisfies the formal design specifications and expected performance goals defined in the system specification model, strictly adheres to the engineering constraints (such as hard-coded resource limits) contained in the system specification model, and fixes all known counterexamples; the code implementation annotations are natural language descriptions generated by the generative language model for the generated business logic code fragments, used to explain how to satisfy the formal design specifications and expected performance goals; the confidence score is the generative language model's self-evaluation score for the generated code in terms of satisfying the specifications and performance goals.

[0052] Specifically, when the adversarial refining module initiates its iterative loop, a generative language model specifically built for code generation and repair tasks is invoked. The model's construction process is as follows: First, a large-scale code generation base model based on the Transformer architecture, pre-trained on a massive general-purpose code corpus, is selected. Second, a dedicated fine-tuning dataset is constructed, consisting of millions of structured "reduction-counterexample-solution" triplets. Here, the "reduction" is a snippet from a high-quality open-source project's requirements document, the "counterexample" is a corresponding, closed performance or logic error report, and the "solution" contains the final commit code that fixes the error and a commit message explaining the fix logic. Finally, the base model is trained using instruction-following fine-tuning techniques, enabling it to learn to jointly generate code, code implementation annotations, and confidence scores based on the reduction and counterexample inputs. The confidence score is achieved by adding a linear layer as a score head on top of the model architecture. This score head is trained together with the main model, learning to predict the probability that the generated code will pass automated tests on historical training data. The confidence score is a comprehensive vector calculated by weighting multiple dimensions of indicators. The specific calculation logic is as follows: .in, It is the average probability value of Logits in the output layer of the generative model, representing the statistical confidence of the model in the generated sequence; It is a normativity score obtained by calculating the structural matching degree between the abstract syntax tree of the generated code and the hard-coded constraints in the system specification; It is based on the historical test pass rate predictions of similar code snippets in the training library. The model finally outputs this vector and a weighted scalar score, such as {"overall": 0.92, "semantic_prob": 0.98, "spec_match": 0.85}, thereby quantitatively evaluating the reliability of the code.

[0053] In its first iteration, the model receives a YAML-formatted system specification model file generated in the previous stage, with an empty set of counterexamples. The model's task is to generate new business logic code based on the business objective of "integrating flash sale coupon logic" and the performance objectives {performance_goals: {qps: 30000, p99_latency_ms: 150}} in the specification. Simultaneously, the model identifies an "engineering constraint" in the specification: "the maximum number of database connection pool connections is hardcoded to 100." Since the model's training data contains numerous solutions to the "low-latency data query" problem, the model recognizes this as a typical caching application scenario and understands that 100 database connections alone cannot support 30,000 QPS. Therefore, the generated Go language function module prioritizes accessing an external Redis caching cluster to verify coupons, thus meeting the 150-millisecond P99 latency target while adhering to the rigid limit on the number of database connections.

[0054] In addition to the code, the deployment candidates output by the model include two additional outputs. The first is a code implementation annotation, which is natural language text generated by the model based on the commit message style from the training data. The annotation reads: "Code implementation annotation: To meet the 150ms P99 latency performance target, Redis is used as a first-level cache to query the coupon status. This can control the average response time of this operation to within 5ms." The second is a confidence score, a structured data object, such as {'overall': 0.92, 'spec_compliance': 0.95, 'performance_achievement': 0.88}. The performance_achievement score of 0.88 reflects the model's understanding from the training data: while the caching strategy is effective, it cannot guarantee that the latency target will be met in all edge scenarios.

[0055] In subsequent iterations, if a counterexample was recorded in the previous round of verification, such as "data inconsistency caused by concurrent writes to the cache under high concurrency," this counterexample will be used as input along with the system specification. If the model matches a counterexample similar to "data race" and its corresponding "distributed lock" solution in its training data, a distributed lock mechanism based on the Redis SETNX command will be introduced into the newly generated code to fix the counterexample.

[0056] This approach utilizes a generative language model to directly translate high-level specifications into concrete, explained code implementations. The model not only generates functional code but also clarifies the design decisions made to meet specific performance and specification goals. Furthermore, it provides quantitative self-assessment through confidence scoring, making the code's intent clear and auditable.

[0057] Furthermore, the adversarial refining module includes a collaborative verification and testing engine and an auditing language model: the verification and testing engine is responsible for performing formal verification, specifically including: generating logical test vectors using a model checker for formal design specifications, and generating stress test vectors using an automated load testing framework for expected performance targets; the auditing language model is a classification and generation dual-task model based on a Transformer encoder-decoder architecture, responsible for performing semantic adversarial auditing, specifically including: reading the system specification model and the code implementation annotations and confidence scores generated by the generative language model, and generating semantic test scenarios containing potential logical vulnerabilities to challenge the annotations and scores; the verification and testing engine also compiles the semantic test scenarios into executable test vectors, and merges them with the logical test vectors and stress test vectors to output a comprehensive adversarial test vector.

[0058] Specifically, upon receiving the deployment candidates output by the generative language model, the verification and testing engine and the auditing language model within the adversarial refining module are launched in parallel. The verification and testing engine performs two tasks. The first is formal verification, which reads the formal design specification regarding coupon state transitions defined in TLA+ format in the system specification model. A model checker integrating the TLA+ toolset analyzes this specification together with the high-level behavioral model of the candidate code. When a path violates the specification, the checker outputs the state transition sequence that caused the violation. This sequence is mapped by a transition layer into a series of ordered API call instructions, forming a logical test vector. The second task is performance verification. The engine reads the expected performance targets {qps: 30000, p99_latency_ms: 150} in the specification and uses an automated load testing framework (such as k6) to generate a stress test vector, which is a test script configured to simulate a 30000 QPS load.

[0059] Meanwhile, the auditing language model performs semantic adversarial auditing, employing a Transformer-based encoder-decoder architecture (e.g., BART or CodeT5). During fine-tuning, the encoder understands the input code implementation annotations and confidence score vectors, while the decoder generates structured semantic test descriptions via autoregression. The training dataset consists of "code intent - potential vulnerability description" pairs. Upon receiving the annotation "...using Redis as a first-level cache...", the decoder, based on pre-trained knowledge, infers common race condition risks in caching scenarios and generates a structured test scenario description containing preconditions, triggering actions, and expected anomalies: "Scenario: During the transaction processing of a user redeeming a coupon, the coupon status in the database is simulated as 'frozen' by the backend system, but the Redis cache fails to update synchronously due to network latency. Verify whether the system will incorrectly allow the user to use the frozen coupon."

[0060] Finally, the verification and testing engine receives this semantic test scenario. The engine's built-in Named Entity Recognition (NER)-based parser identifies the entities and actions in the scenario. A test orchestration template (e.g., a Jinja2 template) receives this parsed information. This template pre-loads a library of test action primitives, a mapping dictionary that defines the correspondence between natural language verbs and underlying fault injection tool APIs. For example, the mapping might be: {"action": "delay", "target": "network", "implementation": "toxiproxy_cli.add_toxic(type="latency", jitter=0)"}. The parser populates the identified entities (such as the target database table name) and action parameters (such as the delay in milliseconds) into template variables. The compiler then calls the corresponding underlying API code snippets, ultimately rendering and generating a Python executable test script that includes steps such as direct database connection modification, artificial delay injection, and API calls. This script, along with the aforementioned logic test vectors and stress test vectors, is organized into a comprehensive set of adversarial test vectors. The specific organization logic is as follows: the stress test vector is used as a background load generator, and a continuous running time axis is set; the logic test vectors and semantic test scenarios are treated as discrete attack events and injected into specific time points on the time axis according to the Poisson distribution time interval, thereby forming a composite test sequence that accurately triggers specific logic paths under extreme load conditions.

[0061] This approach ensures fundamental code quality through formal verification and performance testing, and uncovers semantic vulnerabilities hidden deep within the design logic using a specialized auditing language model. This combined approach goes beyond traditional testing, proactively identifying and verifying non-obvious risks introduced by design decisions, significantly improving workflow reliability.

[0062] Furthermore, the specific implementation steps of the verification and iterative correction loop in the adversarial refinement module are as follows: An isolated logical consistency simulation environment is automatically constructed, with resource configuration based on the resource baseline in the system specification model; candidate workflow code is deployed in the environment, the load model in the system specification model is injected as background traffic, and comprehensive adversarial test vectors are injected as attack traffic; a monitoring observer is deployed to compare the actual operating metrics of the code with the specifications and objectives in the system specification model in real time. When behavioral violations and performance deviations triggered by formal verification are detected, and failures triggered by business logic adversarial auditing where the expected business logic does not match the actual behavior are detected, the violation is recorded as a structured new counterexample; the new counterexample is fed back to the audit language model, which converts the structured new counterexample into a natural language description that the generative language model can understand for repair, and the natural language description serves as a new constraint to initiate the next iteration.

[0063] Specifically, after generating comprehensive adversarial test vectors, the system iteratively enters the verification phase. The system automatically operates in an isolated, configuration-consistent simulation environment using Infrastructure as Code (IaC). The logical topology and resource configuration of this environment are strictly aligned with the resource baseline in the system specification model, and sandbox instances of dependent services of the same version as the production environment are deployed.

[0064] Subsequently, a Docker container image containing the newly generated code was automatically deployed to this environment. The verification and testing engine used a load generation tool (k6) to inject continuous background traffic based on a quantified load model. Simultaneously, comprehensive adversarial test vectors were injected as attack traffic. For example, while the background traffic was running, a Python test script was executed. This script, after a database state change, delayed the arrival of a cache update event by temporarily blocking the network and initiated an API call within this delay window.

[0065] The monitoring observers deployed in the environment (such as Prometheus) compare the observed metrics with the target in real time. After executing the above attack scenario, the monitoring system detected a failure: the candidate workflow code returned an HTTP 200 success response, violating the correctness of the business logic. This failure event was immediately captured and logged as a new structured counterexample, the content of which is a JSON object containing fields such as trigger vector, expected behavior, and actual behavior: {"failure_id": "CF-002", "triggering_vector": "semantic_test_cache_db_inconsistency", "observed_behavior": "API returned success for a frozen coupon", "expected_behavior": "API should return error for a frozen coupon", ...}.

[0066] Finally, this structured new counterexample is fed back into the audit language model. This model integrates a rule- and template-based translation module. It parses the fields in the counterexample JSON, queries a pre-defined "failure mode-remediation strategy" knowledge base, finds the "origin verification" strategy corresponding to the "cache inconsistency" mode, and generates a natural language description using the instruction template: "Remediation instruction: The current code implementation failed to handle the race condition of inconsistency between the cache and database states. After retrieving the coupon state from the cache, an origin verification step must be added to confirm that it has not been modified by an external system during transaction processing." This description, as a new constraint, is input into the generative language model along with the original system specification, initiating a new round of iterative correction loop.

[0067] In a preferred embodiment, the adversarial refining module further includes a metacognitive language model, which is used to: continuously monitor the evolution history of the number and type of counterexamples in the iterative loop; when a preset iterative deadlock pattern is detected, wherein instances of the iterative deadlock pattern include instances where the number of counterexamples fails to converge in N consecutive iterations, and specific counterexamples are alternately repaired and reproduced; pause the iterative loop, and based on the evolution history, analyze the root design flaws that lead to the deadlock; generate a higher-level strategic refactoring instruction to guide the next round of code generation, and inject it into the system specification model.

[0068] Specifically, during the execution of the adversarial refining module, a metacognitive language model (a model trained on numerous software engineering project evolution histories and architectural refactoring cases) monitors the counterexamples generated in each iteration. In a refining iteration of an order processing workflow, the model detected a pre-defined iterative deadlock pattern: in five consecutive iterations (N=5), a performance counterexample concerning "inventory deduction delay under high concurrency" and a logical counterexample concerning "distributed transaction data inconsistency" alternated. The asynchronous message queue scheme used by the generative language model to address performance issues consistently generates data inconsistency counterexamples; conversely, the strong synchronization lock scheme used to address data consistency issues generates performance counterexamples. The metacognitive language model identified this as a typical architectural choice dilemma. It then paused the loop, analyzed the entire deadlock history, and generated a strategic refactoring instruction: "The root design flaw lies in the use of a single data update model. Instruction: Refactor the inventory deduction logic, adopting an eventual consistency architecture pattern that combines optimistic locking with compensating transactions to balance performance and data consistency." This instruction, as a high-priority constraint, was dynamically added to the system specification model, guiding the generative language model to move beyond its current implementation approach and solve the problem from a higher dimension.

[0069] This approach introduces metacognitive supervision, enabling the iterative process to self-reflect and escape local optima. It can proactively identify and resolve deep-seated architectural flaws, rather than merely passively fixing superficial counterexamples, thereby significantly improving convergence efficiency and final code quality in complex problem scenarios.

[0070] This solution constructs an automated "generate-verify-feedback" closed loop. It not only identifies code defects but also automatically translates complex failure scenarios into precise and understandable fixes, driving the generation model to self-correct. This iterative refinement process ensures that workflow code can systematically converge and eliminate known functional, performance, and logical vulnerabilities before deployment.

[0071] Furthermore, after the adversarial refinement module terminates the loop and outputs the final deployment candidate, the deployment decision module's decision logic is specifically used for: quantitatively analyzing the iteration history, analyzing all counterexamples and performance deviation histories recorded in the adversarial loop, calculating risk indicators, which include: formal test coverage, business logic scenario coverage, the source distribution of the final counterexamples, the failure rate of high-confidence regions, and the final performance deviation compared to the expected performance target; generating a deployment risk assessment report, calculating a comprehensive risk score based on the risk indicators, and generating an assessment report containing natural language explanations; performing a decision based on a business acceptance risk threshold, comparing the comprehensive risk score with a preset business acceptance risk threshold; when the comprehensive risk score is lower than the business acceptance risk threshold, outputting an authorization instruction and automatically generating an encrypted signed authorization token, which serves as the credential for initiating the downstream automated deployment process; when the comprehensive risk score is not lower than the business acceptance risk threshold, automatically rejecting the deployment and outputting alarm information to the operations engineer.

[0072] Specifically, after the adversarial refinement module outputs the final deployment candidate, the deployment decision module is activated. This module first quantitatively analyzes the entire iteration history and calculates a set of risk indicators, such as: formal test coverage of 99.8%, business logic scenario coverage of 100%, and failure rate of 2% in high-confidence areas.

[0073] The module then calculates a comprehensive risk score based on these indicators. This calculation is performed using a weighted summation model, with the weights of each indicator predetermined by the committee using the Analytic Hierarchy Process (AHP) based on their criticality to the business. For example, the "failure rate in high-confidence areas" is assigned the highest weight of 0.4 because it reveals unpredictable risks. After normalization, all indicators are weighted and summed to obtain a comprehensive risk score ranging from 0 to 100, such as 8.5. A templated Natural Language Generation (NLG) component generates a deployment risk assessment report based on this score. This component uses a mapping rule engine to map the score range to preset descriptive text fragments and populates the text into the report template, generating an assessment report that includes a natural language explanation such as "Comprehensive Risk Level: Low".

[0074] Finally, the module makes a decision based on a business acceptance risk threshold. It compares the overall risk score of 8.5 with a business acceptance risk threshold (e.g., 15.0) defined by the organization's governance policy. If the score is below the threshold, the module determines the risk is acceptable and generates a JSON Web Token, cryptographically signed with an ECDSA private key, as an authorization token. This token contains a candidate image digest and the risk score and serves as the unique credential for downstream automated deployment processes. If the score is above the threshold, the module automatically rejects the deployment and sends an alert containing a risk report to the operations engineer's alert channel via a webhook.

[0075] In a preferred embodiment, the decision logic of the deployment decision module is further configured to: when the comprehensive risk score is within a preset conditional acceptance range, analyze the deployment risk assessment report to identify the primary risk factor that contributes the most to the comprehensive risk score; automatically generate a runtime mitigation strategy directly corresponding to the primary risk factor, the runtime mitigation strategy being used to restrict and monitor the primary risk factor in the production environment; and output a conditional authorization instruction containing the authorization token and the runtime mitigation strategy.

[0076] Specifically, in one deployment decision, the calculated comprehensive risk score was 18.5. This score is higher than the conventional acceptance threshold of 15.0, but lower than the absolute prohibition threshold of 25.0, thus falling within the preset conditional acceptance range. The deployment decision module then further analyzed the risk assessment report. By analyzing the scores of each sub-item in the weighted model, it identified the primary risk factor as "final performance deviation," specifically manifested as "intermittent latency exceeding 500ms for certain complex query operations in the reconciliation database under high concurrency." The module then automatically generated a runtime mitigation strategy, which is a configuration file for the service mesh (such as Istio). This configuration file defines a request-level timeout rule (timeout: 300ms) for limiting the workflow service, and a circuit breaker rule (circuitBreaker: consequentErrors: 5) for monitoring. Finally, the module outputs a conditional authorization instruction. The instruction's data packet contains not only the JWT authorization token but also this Istio configuration file. Downstream automated deployment systems will apply this mitigation strategy to the service mesh in the production environment while deploying the workflow.

[0077] This solution introduces a more refined, risk-adaptive decision-making mechanism. It avoids a blanket rejection of "gray-scale" releases with manageable risks, and achieves precise hedging of known risks without sacrificing deployment efficiency by automatically generating and bundling runtime security policies.

[0078] This module replaces the traditional pre-deployment review, which relies on human experience and subjective judgment, with a fully automated, quantitative risk assessment process. It provides data-driven, auditable evidence for deployment decisions, transforming critical "deploy / termination" decisions from human judgment to deterministic strategy execution, thereby ensuring the quality and consistency of the change process and establishing a traceable authorization chain for all deployment activities in production environments.

[0079] Furthermore, when outputting the authorization command, the deployment decision module performs an additional task: extracting local state invariants from the formal design specifications in the final deployment candidate, and combining them with the risk indicators in the deployment risk assessment report, mapping and compiling the local state invariants into a set of production environment runtime security observers; this set of security observers is attached to the candidate workflow code and output along with the authorization command; the runtime security observers are used to capture corresponding violation events when the deployed workflow triggers behavior that violates the formal design specifications in the production environment, and when the performance of the deployed workflow touches the risk area indicated in the risk indicators; the runtime security observers format the captured violation events into new production environment structured counterexamples and automatically send the production environment structured counterexamples back to the adversarial refining module; the audit language model receives the production environment structured counterexamples, analyzes the root cause, and elevates them to high-priority business logic test scenarios that need to be executed in all future iterations, triggering a new iterative optimization loop based on the production environment structured counterexamples.

[0080] Specifically, while outputting the authorization token, the deployment decision module initiates an observer compilation task, and a compiler performs a layered transformation task to generate a security observer:

[0081] The first step is local invariant extraction: The compiler front end traverses the abstract syntax tree (AST) of the TLA+ reduction, and does not directly process the full-sequential logic, but instead identifies local state invariants that do not depend on long-history states (e.g., "the coupon state variable S must satisfy at any time"). (”).

[0082] The second step, symbol anchoring and address mapping, introduces a deterministic symbol resolution mechanism. During the compilation phase, the system maintains a "variable-symbol mapping table." For global state variables in the TLA+ specification (such as "CouponState"), the compiler first traverses the abstract syntax tree of candidate code, automatically searching for class member variables with specific domain annotations (such as @StateVar("CouponState")), or finding the corresponding memory symbol address by precisely matching the variable name in the symbol table (DWARF / PDB). After determining the source code variables, the compiler generates anchoring logic based on the language runtime characteristics: for statically compiled languages ​​such as C / C++, the virtual address offset of the .data segment or stack is directly calculated; for managed languages ​​with garbage collection (GC) mechanisms such as Java and Go, the compiler obtains the field offset of the object in the heap memory relative to the object header through the JVMTI or Debug Info interface and hardcodes this offset into the generated eBPF bytecode. The compiler generates an eBPF program based on Uprobes (user-space probes), attaches it to the function entry point, and uses registers or stack frame pointers to capture parameter values ​​at the moment the function call occurs. This method avoids the problem of dynamic object memory address drift caused by GC, thereby establishing a precise anchor from abstract specification to specific runtime state; for example, anchoring the abstract variable "CouponState" to a specific virtual address offset in the stack of a specific microservice process, or to a specific protocol field offset in the payload of a network packet.

[0083] The third step is bytecode generation: The compiler backend converts the extracted local invariants into assertion logic in C language format (such as if (val!=Active&&val!=Used)alert()), and uses the LLVM toolchain to compile it into eBPF bytecode.

[0084] Finally, this bytecode is injected into critical function entry points or network socket filter mount points in the Linux kernel. This conversion mechanism ensures that kernel-mode observers only need to verify the validity of the current "state snapshot" with minimal overhead, thereby generating a set of high-performance security observers for production environments. Simultaneously, based on risk areas (i.e., weak points) identified in the risk report, the compiler generates a dedicated Prometheus alerting rule with stricter alert thresholds to monitor the corresponding code modules.

[0085] The generated eBPF probes and Prometheus alerting rules together constitute a runtime security observer, which is attached to the Kubernetes deployment manifest of the deployment candidate and output along with the JWT authorization token. In the production environment, the runtime security observer (specifically the eBPF probes) non-intrusively monitors inter-service communication in a sidecar mode, verifying in real time whether its behavior complies with formal design specifications.

[0086] Suppose an undiscovered concurrency vulnerability causes a "used" coupon to be reused. An eBPF probe deployed in the sidecar captures this illegal state transition request, immediately formats the complete context of the event into a new production-environment structured counterexample (ID: PROD-CF-001), and automatically posts it back to the adversarial refining module. The audit language model triggers a new round of iterative optimization based on this production-environment structured counterexample.

[0087] This production environment failure example was received by the audit language model. The model employed a hybrid approach based on pattern matching and causal inference, matching event characteristics against a built-in "failure mode knowledge graph" to identify the "concurrent write conflict" pattern. Combined with static code analysis results, it inferred the root cause as "a lack of atomicity constraints at the database level." Based on this, the model generated precise natural language fix instructions and simultaneously abstracted this production failure scenario into a new, high-priority concurrent test case, permanently adding it to the core test library. These fix instructions, along with the new test specification, triggered a completely new iterative optimization loop based on production environment feedback.

[0088] This solution extends quality assurance from limited pre-deployment verification to continuous monitoring and adaptive optimization of the production environment. It establishes a complete closed loop, from identifying unknown risks in the production environment, to automatically analyzing, learning, and solidifying them into future testing protocols, and finally driving the system's self-healing. This mechanism enables the entire platform to learn from real-world failures and continuously evolve, fundamentally improving the system's long-term resilience and reliability.

[0089] This invention provides an intelligent cloud workflow generation and management platform that integrates a large language model. The workflow modeling module replaces the traditional fuzzy requirement definition, constructing a quantitative and objective system specification model through multi-dimensional data fusion, laying a solid data foundation for subsequent work. The adversarial refining module establishes an automated closed loop of generation and verification, iteratively discovering and fixing counterexamples, driving the quality of candidate workflow code to systematically converge towards meeting specification and performance goals. Finally, the deployment decision module transforms the entire refining process into a quantifiable risk assessment report, replacing subjective manual review with data-driven automated threshold decisions. This ensures that only fully verified and risk-controllable code is authorized for deployment, significantly improving the rigor, consistency, and auditability of the entire process.

[0090] Example 2:

[0091] This embodiment provides an intelligent cloud workflow generation and management platform that integrates large language models. In this embodiment, we will take the example of a financial service institution using this platform to upgrade its core interbank transaction reconciliation workflow.

[0092] The organization plans to introduce a new instant payment protocol. The core technical challenge is that the new workflow must meet a high throughput target of 5000 transactions per second (TPS) while ensuring the eventual consistency and immutability of each transaction to comply with stringent financial regulatory requirements. When an operations engineer issues the instruction to "upgrade the transaction reconciliation logic of 'interbank-recon-workflow-v3.0' to support the ISO20022 protocol, ensuring end-to-end data immutability and meeting the 5000 TPS throughput target," the platform's operational modeling module is triggered. This module first pulls the workflow's Java source code from the internal version control system via its API and collects traffic and telemetry data from the monitoring system during peak business periods over the past month. Subsequently, a RoBERTa model fine-tuned on a financial sector operations instruction corpus parses the instruction, extracting formalized business objectives; an abstract syntax tree analysis tool scans existing code to extract engineering constraints; and simultaneously, an ARIMA algorithm analyzes historical transaction rates to construct a quantitative load model. Ultimately, all information is integrated into a system specification model. This process also employs a template-based integration mechanism: based on the keywords "ISO20022" and "data immutability" in the operation and maintenance instructions, the system retrieves the corresponding "financial-grade ledger consistency" TLA+ template from the template library. The system injects hard constraints extracted from static analysis of Java code (such as single-threaded file lock mechanisms) as parameters into the template slots and compares them with soft targets in the operation and maintenance instructions (such as 5000 TPS throughput). If a soft target is found to exceed the physical limits of the hard constraints, the modeling module automatically marks the conflict and generates mandatory architecture refactoring constraints in the model. The processed information is finally encapsulated in a machine-readable system-spec.yaml format.

[0093] Upon receiving the system specification model, the platform's adversarial refinement module initiates its iterative loop. In the first iteration, a generative language model fine-tuned on massive amounts of secure financial transaction code generates Java business logic code based on Merkle trees and WORM (write-once, read-many) storage to meet the "data immutability" specification. This code includes code implementation annotations explaining its design intent and a confidence score vector containing multiple dimensions such as normativity and probability. Subsequently, the module's verification and testing engine and auditing language model start in parallel. The former generates logical test vectors based on formal specifications and load test scripts according to performance targets. The latter uses a Transformer encoder-decoder architecture (such as CodeT5). After reading the code annotations, the decoder uses its pre-trained financial security knowledge to identify the high-risk semantic scenario of "key switching" and generates a corresponding natural language test description. The verification and testing engine compiles this scenario into executable test vectors and merges them with other vectors. In a high-fidelity simulation environment automatically built by the platform, candidate code is deployed and injected with background traffic and adversarial test vectors. The monitoring observer deployed in the environment quickly detected a performance failure: during a key rotation scenario, the system throughput plummeted to 2500 TPS. This failure was recorded as a structured new counterexample and translated by the auditing language model into a clear natural language remediation instruction: "The key rotation logic needs to be refactored into a lock-free design." This instruction, as a new constraint, was fed back to the generative language model to initiate a new round of iterative correction until a preset convergence condition was met (i.e., the number of new counterexamples generated in N consecutive iterations is less than a threshold ε, or the number of iterations reaches a preset maximum security circuit breaker threshold), thus outputting a risk-converged deployment candidate.

[0094] After the adversarial refining module terminates its loop, the platform's deployment decision module is activated. This module quantitatively analyzes the entire iteration history, calculates a set of risk indicators, and, based on a weighted model pre-defined for financial systems, arrives at a comprehensive risk score of 4.2. This score is below the pre-defined acceptance threshold of 5.0, therefore the module determines that the deployment risk is acceptable and immediately generates a JWT authorization token signed with an ECDSA private key. Simultaneously, the module performs an additional task: generating a security observer based on the principle of layered transformation. It first extracts local state invariants regarding "transaction atomicity" from the specification (e.g., "the sum of the amounts for both borrowers and lenders with the same transaction ID must be zero"); then, it performs a two-phase symbolic anchoring: first, it scans the class definition using Java bytecode analysis tools (such as ASM) to find the concrete Java class field (e.g., TransactionEntry.amount) corresponding to the abstract variable in the "transaction atomicity" specification; next, it uses the JVM Tool Interface (JVMTI) to obtain the offset of this field in the object's memory layout, or the byte offset in the serialized Kafka message body; finally, it compiles the anchored logic into filter rules executable by the Sidecar agent (such as Wasm plugins or Lua scripts). Months later, a rare network partition event in the production environment caused a breach in transaction atomicity. The Sidecar security watcher immediately captured this violation, formatted it as a new structured counterexample, and automatically posted it back to the adversarial refining module. Upon receiving this production counterexample, the audit language model within the module analyzes and infers the root cause, elevates it to a high-priority test scenario that must be executed in all future iterations, generates repair instructions, and automatically triggers an iterative optimization loop based on feedback from the real production environment.

[0095] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the scope of protection defined in the claims.

Claims

1. An intelligent cloud workflow generation and management platform integrating large language models, characterized in that, include: Operating condition modeling module: Collects existing workflow code, real-time user request traffic characteristic data, and cloud infrastructure monitoring telemetry data; Based on existing workflow code, real-time user request traffic characteristic data, and cloud infrastructure monitoring telemetry data, a system specification model is constructed that includes formal design specifications and expected performance targets; the working condition modeling module responds to the workflow identifier of the operation and maintenance instructions and pulls the existing workflow code of the current production environment through the API interface; The system collects real-time user request traffic characteristic data related to workflows within a preset time window. This data includes request rate timing, response latency, and request type distribution. It also collects cloud infrastructure monitoring telemetry data, including computing resource utilization, memory consumption, and network throughput. The system integrates existing workflow code, real-time user request traffic characteristic data, cloud infrastructure monitoring telemetry data, and operation and maintenance instructions. Natural language processing is used to parse the operation and maintenance instructions and extract formalized business objectives. Static code analysis is used to scan the abstract syntax tree of the existing workflow code and extract hard-coded rigid constraints as engineering constraints. Feature engineering is used to transform the real-time user request traffic characteristic data and cloud infrastructure monitoring telemetry data into a quantified load model and resource baseline. Finally, the formalized business objectives, engineering constraints, and quantified load model and resource baseline are integrated into a unified, machine-readable structured system specification model. Adversarial Refinement Module: Executes an iterative loop, generating candidate workflow code based on the system specification model and counterexamples using a generative language model within the loop; and generates and executes reproducible test vectors based on formal design specifications and expected performance goals through a verification and testing engine. Test vectors are used to examine the specification compliance and performance of candidate workflow code under simulated operating conditions, and new counterexamples are recorded. The loop terminates when no new counterexamples are generated for a consecutive preset number of rounds, and when the number of iterations reaches a preset threshold, and outputs the final deployment candidate. The adversarial refinement module includes a collaborative verification and testing engine and an auditing language model. The verification and testing engine is responsible for performing formal verification, specifically including: generating logical test vectors using a model checker for formal design specifications, and generating stress test vectors using an automated load testing framework for expected performance targets. The auditing language model is a classification and generation dual-task model based on a Transformer encoder-decoder architecture, responsible for performing semantic adversarial auditing, specifically including: reading the system specification model and the code implementation annotations and confidence scores generated by the generative language model, and generating semantic test scenarios containing potential logical vulnerabilities to challenge the annotations and scores. The verification and testing engine also compiles the semantic test scenarios into executable test vectors, and merges them with the logical test vectors and stress test vectors to output a comprehensive adversarial test vector. Deployment Decision Module: Quantitatively analyzes all recorded counterexamples and performance deviations, generates a deployment risk assessment report; executes decisions based on risk thresholds, and when the risk level is lower than a preset threshold, outputs an authorization instruction to initiate the production deployment process of candidate workflow code.

2. The intelligent cloud workflow generation and management platform integrating a large language model according to claim 1, characterized in that, In the iterative loop of the adversarial refining module, the specific task of the generative language model is: The system receives the system specification model and counterexamples from the previous iteration. If it is the first iteration, there are no counterexamples. A deployment candidate body is generated, which includes candidate workflow code, code implementation annotations, and a confidence score. The candidate workflow code is newly generated business logic code that satisfies the formal design specifications and expected performance goals defined in the system specification model, complies with the engineering constraints contained in the system specification model, and fixes all known counterexamples. The code implementation annotations are natural language descriptions generated by the generative language model for the generated business logic code fragments, used to explain how they satisfy the formal design specifications and expected performance goals. The confidence score is the generative language model's self-evaluation score for the generated code in terms of satisfying the specifications and performance goals.

3. The intelligent cloud workflow generation and management platform integrating a large language model according to claim 1, characterized in that, The specific implementation steps of the verification and iterative correction loop in the adversarial refining module are as follows: An isolated, logically consistent simulation environment is automatically constructed, the resource configuration of which is based on the resource baseline in the system specification model; candidate workflow code is deployed in the environment, the load model in the system specification model is injected as background traffic, and a comprehensive adversarial test vector is injected as attack traffic; A monitoring observer is deployed to compare the actual operating metrics of the code with the specifications and objectives in the system specification model in real time. When a behavioral violation or performance deviation triggered by formal verification is detected, and a failure triggered by business logic resistance auditing where the expected business logic does not match the actual failure is detected, the violation is recorded as a new structured counterexample. The new counterexample is fed back to the audit language model, which converts the new structured counterexample into a natural language description that the generative language model can understand for repair. The natural language description serves as a new constraint to initiate the next iteration.

4. The intelligent cloud workflow generation and management platform integrating a large language model according to claim 1, characterized in that, After the adversarial refining module terminates the loop and outputs the final deployment candidate, the deployment decision module's decision logic is specifically used for: The iteration history is analyzed quantitatively, including all counterexamples and performance deviations recorded in the adversarial loop. Risk indicators are calculated, including: formal test coverage, business logic scenario coverage, the source distribution of the final counterexamples, the failure rate of the high confidence region, and the final performance deviation compared to the expected performance target. Generate a deployment risk assessment report, calculate a comprehensive risk score based on the risk indicators, and generate an assessment report containing natural language explanations; execute a decision based on a business acceptance risk threshold, comparing the comprehensive risk score with a preset business acceptance risk threshold; When the overall risk score is lower than the business acceptance risk threshold, an authorization instruction is output and an encrypted authorization token is automatically generated. The authorization token is used as a credential to initiate the downstream automated deployment process. If the overall risk score is not lower than the business acceptance risk threshold, the deployment will be automatically rejected and an alarm message will be sent to the operations engineer.

5. The intelligent cloud workflow generation and management platform integrating a large language model according to claim 4, characterized in that, When outputting the authorization command, the deployment decision module performs an additional task: extracting local state invariants from the formal design specifications in the final deployment candidate, and combining them with the risk indicators in the deployment risk assessment report, mapping and compiling the local state invariants into a set of production environment runtime security observers; this set of security observers is attached to the candidate workflow code and output along with the authorization command; the runtime security observers are used to capture corresponding violation events when the deployed workflow triggers behavior that violates the formal design specifications in the production environment, and when the performance of the deployed workflow touches the risk area indicated in the risk indicators; the runtime security observers format the captured violation events into new production environment structured counterexamples and automatically send the production environment structured counterexamples back to the adversarial refinement module; the audit language model receives the production environment structured counterexamples, analyzes the root cause, and elevates them to high-priority business logic test scenarios that need to be executed in all future iterations, triggering a new iterative optimization loop based on the production environment structured counterexamples.

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