An intelligent model system for automatically generating standard protocol interfaces

Abstract

This invention relates to the field of computer data processing technology, and more particularly to an intelligent model system for automatically generating standard protocol interfaces. The system includes a requirement acquisition module that acquires user-input requirement information and converts it into standardized requirement parameters. A protocol knowledge base module outputs a compliance rule set based on the target protocol type. An interface generation module automatically generates interface source code, configuration files, and interface documentation based on the standardized requirement parameters and the compliance rule set. A verification module verifies and optimizes the generated interfaces, and a self-learning module updates the system's parsing logic and rule content based on the verification results. The system employs a dual-path fusion technology of natural language processing and structured parsing to achieve accurate requirement parsing, intelligent matching of protocol rules through a protocol knowledge graph, and automatic code generation based on symbolic reasoning and neural code generation technology. This forms a complete closed loop of requirement analysis, protocol adaptation, code generation, and verification optimization, effectively improving the efficiency and quality of interface development.

Description

Technical Field

[0001] This invention relates to the field of computer data processing technology, and in particular to an intelligent model system that automatically generates standard protocol interfaces. Background Technology

[0002] Standard protocol interfaces serve as the cornerstone of inter-system communication. Their implementation typically relies on manual coding techniques, where engineers parse the text descriptions based on formal protocol specification documents and gradually translate them into concrete code or configuration files. This manual process stems from the semantic and syntactic complexity of protocol definitions, requiring engineers to use their professional knowledge to deeply understand them and accurately map standard rules to the actual interface. Simultaneously, manual review and testing are used to verify the consistency between the implementation and the specification, ensuring the correctness and interoperability of the interface in specific application scenarios.

[0003] Existing technologies based on manually writing standard protocol interfaces suffer from the following pain points: In a microservice architecture, the system typically consists of dozens of independent services that need to interact frequently with each other via standard protocol interfaces such as RESTful and gRPC. When new business functions require cross-service collaboration, developers must first communicate with multiple parties to confirm the details of the interface requirements, then thoroughly learn the syntax specifications and data serialization rules of each target protocol, and finally write the interface definition file, implementation code, and configuration documentation line by line. Taking the order service's need to call the user service to retrieve details as an example, developers need to manually define the field mapping of the gRPC proto file, implement the serialization logic for both Java and Python, and ensure that exception handling conforms to the protocol specifications. This highly manual and repetitive process not only consumes a lot of development time but also limits the service integration progress to the individual development speed, ultimately affecting the overall project delivery cycle. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent model system for automatically generating standard protocol interfaces. This system solves the technical problems of low development efficiency and significantly extended project cycles caused by relying on manual writing of standard protocol interfaces, which involves multiple steps such as requirement analysis, protocol learning, and code writing.

[0005] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows:

[0006] This invention provides an intelligent model system for automatically generating standard protocol interfaces, comprising:

[0007] The requirement acquisition module obtains requirement information from user input and outputs the requirement information to the requirement parsing module.

[0008] The requirement parsing module receives requirement information from the requirement acquisition module, converts the requirement information into standardized requirement parameters including parameter names, data types, constraints, and target protocol types, and outputs the standardized requirement parameters to the protocol knowledge base module and the interface generation module.

[0009] The protocol knowledge base module receives the target protocol type from the standardized requirement parameters from the requirement parsing module, and outputs the corresponding compliance rule set to the interface generation module based on the target protocol type.

[0010] The interface generation module receives standardized requirement parameters from the requirement parsing module and a compliance rule set from the protocol knowledge base module. Based on the standardized requirement parameters and the compliance rule set, it generates interface source code, configuration files, and interface documentation, and outputs the interface source code and configuration files to the verification module.

[0011] The verification module receives the interface source code and configuration file from the interface generation module, verifies and optimizes the interface source code and configuration file, generates a verification report and optimized interface file, and outputs the verification report for system optimization.

[0012] The self-learning module receives verification reports from the verification module and updates the parsing logic of the requirement parsing module and the rule content stored in the protocol knowledge base module based on the verification reports.

[0013] Furthermore, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes a requirement parsing module comprising:

[0014] The natural language processing unit receives unstructured text from the demand information from the demand acquisition module, performs sequence annotation on the unstructured text, extracts entity parameters, and outputs the entity parameters to the parameter fusion unit.

[0015] The structured parsing unit receives structured data from the requirement information from the requirement acquisition module, parses the structured data, and outputs the parsing results to the parameter fusion unit.

[0016] The parameter fusion unit receives entity parameters from the natural language processing unit and parsing results from the structured parsing unit. It performs confidence-weighted fusion of the entity parameters and parsing results, generates standardized requirement parameters, and outputs them to the protocol knowledge base module and the interface generation module.

[0017] Furthermore, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes a protocol knowledge base module comprising:

[0018] Protocol knowledge graph, which stores the relationship edges between protocol syntax element entity nodes and protocol rules;

[0019] The graph matching unit receives the extracted entity parameters from the natural language processing unit of the requirement parsing module, uses the entity parameters as query vectors to perform multi-hop traversal in the protocol knowledge graph, searches for semantically similar entity nodes, and outputs the matching results to the correction unit.

[0020] The correction unit receives the matching results from the graph matching unit, calculates the semantic similarity score between the entity parameters and the graph nodes, and when the score is lower than a preset threshold, activates the graph-guided attention mechanism to correct the entity parameters and outputs the corrected entity parameters to the natural language processing unit.

[0021] Furthermore, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes a protocol knowledge base module comprising:

[0022] The version sniffer periodically accesses a predefined protocol standard publication address, identifies potential updates by comparing the hash values ​​of consecutive version documents, and outputs update signals to the differential analyzer.

[0023] The differential analyzer receives update signals from the version sniffer, compares the abstract syntax trees of the old and new versions of the document, locates the changes in syntax rules, and outputs the changes to the graph update unit.

[0024] The graph update unit receives change points from the differential analyzer, updates the protocol knowledge graph with the change points as new nodes or relation edges, and adds time decay weights to the new and changed graph elements.

[0025] Furthermore, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes an interface generation module comprising:

[0026] The symbolic reasoning unit receives a set of compliance rules from the protocol knowledge base module and standardized requirement parameters from the requirement parsing module. It uses first-order logic predicates to formally describe data types and constraints, solves the problem through the theorem prover to generate intermediate representations, and outputs the intermediate representations to the neural code generation unit.

[0027] The neural code generation unit receives intermediate representations from the symbolic reasoning unit, uses a graph neural network structure to encode the intermediate representations into feature vectors, and a decoder based on the Transformer architecture generates interface source code based on the feature vectors and outputs the interface source code to the verification module.

[0028] Furthermore, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes a verification module comprising:

[0029] The test case generator receives a set of compliance rules from the interface generation module, generates a sequence of test cases, and outputs the sequence of test cases to the sandbox executor.

[0030] The digital twin sandbox receives interface source code and configuration files from the interface generation module and builds a virtual deployment instance.

[0031] The sandbox executor receives test case sequences from the test case generator, injects the test case sequences into the virtual deployment instance, and outputs monitoring data to the analyzer.

[0032] The analyzer receives monitoring data from the sandbox executor, compares the monitoring data with the protocol specifications, generates a verification report and an optimized interface file, and outputs the verification report for system optimization.

[0033] Furthermore, the intelligent model system for automatically generating standard protocol interfaces of the present invention also includes:

[0034] The symbolic reasoning unit of the interface generation module receives a set of compliance rules from the protocol knowledge base module and standardized requirement parameters from the requirement parsing module, and generates a first-generation intermediate representation based on the set of compliance rules and standardized requirement parameters.

[0035] The neural code generation unit receives the first-generation intermediate representation from the symbolic reasoning unit, generates the initial interface code based on the first-generation intermediate representation, and outputs the initial interface code to the verification module.

[0036] The verification module receives the initial interface code from the interface generation module, verifies the initial interface code, generates a correction instruction when the verification fails, and outputs the correction instruction to the symbolic reasoning unit of the interface generation module.

[0037] The symbolic reasoning unit receives correction instructions from the verification module, re-solves the constraints according to the correction instructions, and generates the second-generation intermediate representation.

[0038] The neural code generation unit receives the second-generation intermediate representation from the symbolic reasoning unit, generates updated interface code based on the second-generation intermediate representation, and outputs the updated interface code to the verification module. This process is repeated until the verification module outputs a verification pass signal.

[0039] Furthermore, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes a self-learning module comprising:

[0040] The policy network receives verification reports from the verification module and satisfaction scores from user input. Based on the current interface, it generates task feature vectors, error type distributions in the verification report, and user satisfaction scores, and generates parameter update instructions, which are then output to the parameter update unit.

[0041] The parameter update unit receives parameter update instructions from the policy network and updates the neural network model parameters of the requirement parsing module and the neural code generation unit parameters of the interface generation module according to the parameter update instructions.

[0042] Furthermore, the intelligent model system for automatically generating standard protocol interfaces of the present invention also includes:

[0043] The conflict case recording unit receives verification failure information from the analyzer of the verification module, records the combination of input parameters that caused the interface response to be abnormal, extracts key features, and outputs key features to the temporary node insertion unit.

[0044] The temporary node insertion unit receives key features from the conflict case record unit, inserts the key features as temporary nodes into the protocol knowledge graph, and creates relationship edges from the temporary nodes to the relevant protocol rule nodes.

[0045] The parameter feature checking unit receives extracted parameter features from the natural language processing unit of the requirement parsing module and key features from the temporary node insertion unit. It checks the similarity between the parameter features and the key features. When the similarity exceeds a preset threshold, it outputs a confirmation prompt to the interactive interface.

[0046] Furthermore, the intelligent model system for automatically generating standard protocol interfaces of the present invention also includes:

[0047] The statistics unit receives protocol rule call records from the interface generation module, counts the call frequency and success rate of different protocol rules, generates a protocol attention weight vector, and outputs the protocol attention weight vector to the protocol evolution tracking unit.

[0048] The protocol evolution tracking unit receives the protocol attention weight vector from the statistics unit and adjusts the crawling frequency and update depth of the web crawler according to the protocol attention weight vector.

[0049] The protocol knowledge graph receives protocol attention weight vectors from the statistical unit and sorts the rule nodes according to the protocol attention weight vectors when responding to queries.

[0050] Beneficial effects of this invention;

[0051] This invention automates requirement understanding through a dual-path collaborative processing mechanism in the requirement parsing module. The natural language processing unit and the structured parsing unit extract parameter information from unstructured text and structured data, respectively. The parameter fusion unit generates standardized requirement parameters through confidence-weighted fusion, effectively avoiding misunderstanding biases and communication costs associated with manual requirement analysis. The protocol knowledge base module leverages the graph structure storage and real-time matching capabilities of the protocol knowledge graph. The graph matching unit achieves accurate mapping between parameters and protocol rules through multi-hop traversal. Combined with the dynamic update mechanism of the version sniffer and differential analyzer, the system possesses the ability to continuously adapt to protocol evolution, significantly reducing protocol learning costs. The interface generation module directly transforms requirement parameters into executable code through the formal description of the symbolic reasoning unit and the deep learning generation capabilities of the neural code generation unit. The verification module achieves closed-loop testing and optimization of the generated interfaces through a digital twin sandbox. The self-learning module dynamically adjusts model parameters based on verification feedback, forming an end-to-end automated pipeline from requirement input to compliant interface output. Ultimately, this significantly reduces error rates and project delays caused by manual intervention while improving interface development efficiency. Attached Figure Description

[0052] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the accompanying drawings without creative effort.

[0053] Figure 1 A schematic diagram of the system architecture for an intelligent model system that automatically generates standard protocol interfaces. Detailed Implementation

[0054] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0055] Please see Figure 1 The present invention provides an intelligent model system for automatically generating standard protocol interfaces, comprising:

[0056] The requirement acquisition module obtains requirement information from user input and outputs the requirement information to the requirement parsing module.

[0057] The requirement parsing module receives requirement information from the requirement acquisition module, converts the requirement information into standardized requirement parameters including parameter names, data types, constraints, and target protocol types, and outputs the standardized requirement parameters to the protocol knowledge base module and the interface generation module.

[0058] The protocol knowledge base module receives the target protocol type from the standardized requirement parameters from the requirement parsing module, and outputs the corresponding compliance rule set to the interface generation module based on the target protocol type.

[0059] The interface generation module receives standardized requirement parameters from the requirement parsing module and a compliance rule set from the protocol knowledge base module. Based on the standardized requirement parameters and the compliance rule set, it generates interface source code, configuration files, and interface documentation, and outputs the interface source code and configuration files to the verification module.

[0060] The verification module receives the interface source code and configuration file from the interface generation module, verifies and optimizes the interface source code and configuration file, generates a verification report and optimized interface file, and outputs the verification report for system optimization.

[0061] The self-learning module receives verification reports from the verification module and updates the parsing logic of the requirement parsing module and the rule content stored in the protocol knowledge base module based on the verification reports.

[0062] The intelligent model system that automatically generates standard protocol interfaces achieves end-to-end automated processing from requirement input to compliant interface output through a modular architecture. In the initial stage, the requirement acquisition module receives user-input requirement information, which exists in the form of natural language text or structured data. For example, in a microservice development scenario, a user might describe "developing a user query interface that supports retrieving name and age by user ID, using the RESTful protocol." The requirement acquisition module then passes the requirement information to the requirement parsing module, which employs a dual-path collaborative processing mechanism to improve parsing accuracy.

[0063] The requirements parsing module comprises a natural language processing unit and a structured parsing unit, handling unstructured text and structured data respectively. The natural language processing unit extracts entity parameters using sequence labeling techniques, such as identifying "user ID" from user descriptions as a parameter name and mapping it to an integer data type. The structured parsing unit parses JSON or tabular inputs, extracting field constraints. The parameter fusion unit performs confidence-weighted fusion of the two types of outputs to generate standardized requirements parameters, including parameter names, data types, constraints, and target protocol types. This design avoids misunderstandings inherent in manual requirements analysis. For example, in a scenario where an order service calls a user service, the fusion unit integrates text descriptions and template data, outputting standardized parameters for subsequent modules to use.

[0064] The protocol knowledge base module receives the target protocol type from standardized requirement parameters and outputs a compliance rule set based on the protocol knowledge graph. The protocol knowledge graph stores the relationship edges between protocol syntax element entity nodes and protocol rules, such as the HTTP method node and status code rule edge for the RESTful protocol. The graph matching unit uses the extracted entity parameters as query vectors and performs multi-hop traversal in the knowledge graph to find semantically similar nodes. When the semantic similarity score is lower than a preset threshold, the correction unit activates the graph-guided attention mechanism to correct the parameter mapping relationship. The version sniffer periodically accesses the protocol standard release address, identifies updates through hash value comparison, the differential analyzer compares the abstract syntax trees of the old and new versions of the documents to locate change points, and the graph update unit incrementally updates the knowledge graph nodes to ensure the system adapts to protocol evolution.

[0065] The interface generation module generates interface source code, configuration files, and interface documentation based on standardized requirement parameters and compliance rule sets. The symbolic reasoning unit uses first-order logic predicates to formally describe data types and constraints, and generates intermediate representations through a theorem prover. The neural code generation unit uses a graph neural network encoder to encode the intermediate representations into feature vectors, and a decoder based on the Transformer architecture to generate the interface source code. In the IoT device status reporting scenario, the symbolic reasoning unit formalizes the mapping constraints between device IDs and status codes into logical rules, and the neural code generation unit outputs gRPC interface code including streaming responses.

[0066] The verification module constructs a virtual deployment instance using a digital twin sandbox to verify and optimize the interface source code and configuration files. The test case generator generates test case sequences based on a compliance rule set, the sandbox executor injects these sequences into the virtual instance, and the analyzer compares monitoring data with protocol specifications to generate a verification report. When an anomaly is detected, such as a missing status code enumeration in the interface response, the verification module generates correction instructions to drive iterative optimization in the interface generation module until a verification pass signal is output.

[0067] The self-learning module updates the parsing logic of the requirements parsing module and the rules of the protocol knowledge base module based on the verification report. The policy network integration interface generates task feature vectors, error type distributions, and user satisfaction scores to generate parameter update instructions. The parameter update unit adjusts the parameters of the neural network model in the requirements parsing module and the parameters of the neural code generation unit. The conflict case recording unit extracts key features of verification failure information, the temporary node insertion unit adds the features as nodes to the knowledge graph, and the parameter feature checking unit compares similarities in subsequent parsing, triggering interactive confirmation prompts to form a continuous optimization closed loop.

[0068] The system's modules collaborate closely through data flow. Requirement analysis results drive protocol knowledge retrieval, verification feedback optimizes code generation strategies, and self-learning data updates the analysis logic. Under a microservice architecture, the system achieves a minute-level workflow for interface definition, code generation, and testing, improving development efficiency and reducing human error rates.

[0069] Specifically, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes a requirement parsing module comprising:

[0070] The natural language processing unit receives unstructured text from the demand information from the demand acquisition module, performs sequence annotation on the unstructured text, extracts entity parameters, and outputs the entity parameters to the parameter fusion unit.

[0071] The structured parsing unit receives structured data from the requirement information from the requirement acquisition module, parses the structured data, and outputs the parsing results to the parameter fusion unit.

[0072] The parameter fusion unit receives entity parameters from the natural language processing unit and parsing results from the structured parsing unit. It performs confidence-weighted fusion of the entity parameters and parsing results, generates standardized requirement parameters, and outputs them to the protocol knowledge base module and the interface generation module.

[0073] The requirements parsing module plays a crucial role in the intelligent model system that automatically generates standard protocol interfaces. It transforms user-input requirements into standardized parameters that can be processed by the machine. This module employs a dual-processing mechanism to parse unstructured text and structured data separately, ultimately merging them to output unified, standardized requirements parameters. This lays the foundation for subsequent protocol matching and code generation.

[0074] The Natural Language Processing (NLP) unit is responsible for processing the unstructured text portion of user input. For example, in a microservice development scenario, a user might describe "developing a user query interface that supports retrieving name and age by user ID." This unit employs sequence labeling technology, specifically using a named entity recognition model to identify key entities in the text, such as parameter names, data types, and constraints. The sequence labeling process utilizes a pre-trained language model to segment and semantically analyze the input text, labeling entity boundaries and types word by word to extract structured entity parameters. This approach effectively captures the implicit semantics in natural language, avoids subjective biases inherent in manual parsing, and improves the automation level of requirement understanding.

[0075] The structured parsing unit processes structured data from user input in parallel, such as JSON-formatted configuration templates or tabular field definitions. This unit identifies data format specifications through a parser, deconstructs nested fields layer by layer, and extracts elements such as parameter names, data types, and constraints. During parsing, the unit verifies the integrity of the data structure, for example, checking whether required fields exist or whether data types conform to predefined specifications. The output of the structured parsing unit complements that of the natural language processing unit, ensuring the system is compatible with various input formats and adapts to the expression habits of different users.

[0076] The parameter fusion unit receives entity parameters extracted by the natural language processing unit and parsing results output by the structured parsing unit, and performs a confidence-weighted fusion operation. The fusion process generates comprehensive parameters based on the confidence scores output by each unit, such as the probability score for entity recognition from the natural language processing unit and the reliability index for data verification from the structured parsing unit. The weighting strategy prioritizes data from high-confidence sources, and calibration is performed through semantic similarity comparison when conflicts occur. The final output of the parameter fusion unit includes standardized requirement parameters including parameter names, data types, constraints, and target protocol types, providing consistent and reliable input for the protocol knowledge base module and the interface generation module.

[0077] The logical relationship between the requirement parsing module, the structured parsing unit, and the parameter fusion unit is a pipelined collaboration model. The natural language processing unit and the structured parsing unit parse the requirements from different dimensions, while the parameter fusion unit integrates multi-source information to eliminate ambiguity. In practical applications of terminal products, such as processing application installation requests submitted by users through the intelligent question-and-answer module, the natural language processing unit identifies the application name in "WeChat installation," the structured parsing unit parses the hardware configuration table uploaded by the user, and the parameter fusion unit merges the two types of information into standardized parameters to drive the subsequent interface generation process. This design effectively reduces manual intervention in the requirement transformation process and improves the system's adaptability in complex scenarios.

[0078] Specifically, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes a protocol knowledge base module comprising:

[0079] Protocol knowledge graph, which stores the relationship edges between protocol syntax element entity nodes and protocol rules;

[0080] The graph matching unit receives the extracted entity parameters from the natural language processing unit of the requirement parsing module, uses the entity parameters as query vectors to perform multi-hop traversal in the protocol knowledge graph, searches for semantically similar entity nodes, and outputs the matching results to the correction unit.

[0081] The correction unit receives the matching results from the graph matching unit, calculates the semantic similarity score between the entity parameters and the graph nodes, and when the score is lower than a preset threshold, activates the graph-guided attention mechanism to correct the entity parameters and outputs the corrected entity parameters to the natural language processing unit.

[0082] When constructing the protocol knowledge graph, syntactic elements from standard protocol documents are extracted as entity nodes. For example, HTTP methods GET and POST are defined as independent nodes, and protocol rules, such as status code mappings, are connected as relational edges to form a semantically related graph structure. In the graph storage design, entity node attributes include protocol type and data format constraints, while relational edges carry rule priority and version identifiers, supporting fast retrieval in multi-protocol mixed scenarios. This graph structure enables the system to dynamically reflect the dependencies between protocol specifications. For example, in a microservice architecture that simultaneously adapts to RESTful and gRPC protocols, the graph achieves rule sharing through inheritance edges between nodes.

[0083] After receiving entity parameters extracted by the natural language processing unit, the graph matching unit vectorizes the parameters and uses them as query input to the protocol knowledge graph, executing a breadth-first multi-hop traversal algorithm. During the traversal, the query vector and the graph node embeddings are compared using cosine similarity calculation to identify semantically similar entity nodes. For example, the user input parameter "user identifier" may match the "user_id" node in the graph and its associated integer data type rules. Multi-hop traversal iteratively expands neighbor nodes, covering both directly matched and indirectly associated protocol rules, avoiding semantic omissions caused by single matches and improving the completeness of parameter mapping.

[0084] The correction unit calculates the semantic similarity score between entity parameters and graph nodes based on the matching results output by the graph matching unit, and uses a pre-trained semantic model to evaluate the distance in the vector space. When the score is lower than a preset threshold, the graph-guided attention mechanism is activated. This mechanism adjusts the focus area of ​​the parameter vector based on the importance weights of nodes in the graph. For example, higher attention weights are applied to nodes with significant protocol version differences, thereby correcting parameter mapping biases. The corrected entity parameters are fed back to the natural language processing unit to form a closed-loop optimization. For example, in the order service call scenario, the initially parsed "customer number" parameter is aligned with the "customer_id" rule node in the protocol knowledge graph after correction, avoiding type conflicts during interface generation.

[0085] Each step is tightly integrated through data flow. The protocol knowledge graph provides a structured rule base, the graph matching unit performs the initial mapping between parameters and rules, and the correction unit handles edge cases and semantic ambiguities, forming a progressive processing chain from requirement parsing to protocol adaptation. In practical applications, for example, the system running on the terminal quickly locates the peripheral driver protocol rules through multi-hop traversal and corrects the parameter mapping of domestic hardware using an attention mechanism, supporting the automatic generation of cross-platform interfaces. This invention reduces the reliance on manual protocol learning and improves the efficiency of protocol compliance processing in interface development through a machine-readable graph and adaptive correction mechanism.

[0086] Specifically, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes a protocol knowledge base module comprising:

[0087] The version sniffer periodically accesses a predefined protocol standard publication address, identifies potential updates by comparing the hash values ​​of consecutive version documents, and outputs update signals to the differential analyzer.

[0088] The differential analyzer receives update signals from the version sniffer, compares the abstract syntax tree of the old and new versions of the document, locates the change points in the syntax rules, and outputs the change points to the graph update unit.

[0089] The graph update unit receives change points from the differential analyzer, updates the protocol knowledge graph with the change points as new nodes or relation edges, and adds time decay weights to the new and changed graph elements.

[0090] The version sniffer uses a scheduler to set up timed tasks to access pre-configured protocol standard publishing addresses at fixed intervals, such as periodically crawling official documentation repositories for RESTful or gRPC protocols. During each access, the system calculates the hash value of the currently retrieved version document, generates a unique identifier using the SHA-256 algorithm, and compares it with the previously stored hash value. If the hash values ​​do not match, a potential update is identified, and an update signal is triggered and transmitted to the differential analyzer. This hash comparison mechanism can efficiently identify subtle changes in document content, avoid misjudgments caused by metadata changes, and provide reliable input for subsequent analysis.

[0091] After receiving the update signal from the version sniffer, the differential analyzer initiates the abstract syntax tree parsing process, converting the old and new versions of the document into structured tree representations. During parsing, the system traverses the syntax tree nodes, comparing key elements such as protocol method definitions, parameter constraint rules, or status code mappings, and uses a tree difference algorithm to locate newly added, deleted, or modified syntax rule nodes. For example, when a new streaming specification is detected in the gRPC protocol, the differential analyzer identifies the streaming method node and its associated serialization rule change point, and encapsulates the change point into a structured data packet and sends it to the graph update unit.

[0092] After obtaining change point data from the differential analyzer, the graph update unit performs incremental update operations on the protocol knowledge graph. The system maps change points to entity nodes or relation edges in the graph; for example, it inserts new protocol rules as nodes and establishes dependency edges with existing nodes. During the update process, the system assigns time-decay weights to newly added or modified graph elements. These weights are dynamically adjusted based on the element's introduction time, with recently changed rules having higher weights, while the weights of historical rules gradually decrease with each iteration cycle. This weighting mechanism ensures that the graph prioritizes recommending the latest protocol specifications during queries, while retaining historical rules for backtracking compatibility checks, thus improving the knowledge base's adaptability in protocol evolution scenarios.

[0093] Each step is tightly integrated through data flow. The version sniffer provides the foundation for update detection, the differential analyzer enables precise change location, and the knowledge graph update unit completes knowledge base synchronization, forming a closed-loop processing chain from protocol change identification to knowledge integration. In microservice or IoT device management scenarios, this system can automatically adapt to protocol version upgrades. For example, when the HTTP / 2 protocol adds header compression rules, the knowledge graph updates the compression algorithm nodes in real time, driving the interface generation module to output compliant code, reducing the risk of delays caused by manual intervention.

[0094] Specifically, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes an interface generation module comprising:

[0095] The symbolic reasoning unit receives a set of compliance rules from the protocol knowledge base module and standardized requirement parameters from the requirement parsing module. It uses first-order logic predicates to formally describe data types and constraints, solves the problem through the theorem prover to generate intermediate representations, and outputs the intermediate representations to the neural code generation unit.

[0096] The neural code generation unit receives intermediate representations from the symbolic reasoning unit, uses a graph neural network structure to encode the intermediate representations into feature vectors, and a decoder based on the Transformer architecture generates interface source code based on the feature vectors and outputs the interface source code to the verification module.

[0097] The interface generation module, within the intelligent model system that automatically generates standard protocol interfaces, undertakes the core task of transforming standardized requirement parameters into executable code. This module achieves the mapping from logical constraints to source code through the collaborative work of the symbolic reasoning unit and the neural code generation unit. The symbolic reasoning unit receives the compliance rule set from the protocol knowledge base module and the standardized requirement parameters from the requirement parsing module. It first formalizes the data types and constraints in the parameters, constructing a descriptive framework using first-order logic predicates. For example, it represents the integer parameter constraints in the user query interface as atomic formulas in predicate logic, and solves the constraints using a theorem prover to generate intermediate representations as the basis for subsequent code generation.

[0098] In the symbolic reasoning unit, the formal description process involves mapping parameter names extracted from natural language to logical variables, converting data types into domain constraints in the predicate definition, and combining constraints into compound formulas using logical connectors. The theorem prover verifies the satisfiability of the predicate formulas based on resolution principles or table derivation algorithms. When constraint conflicts are detected, the parameter mapping strategy is backtracked and adjusted, ultimately outputting a structured intermediate representation, such as an abstract syntax tree or intermediate code snippets, ensuring logical consistency. This step provides the neural code generation unit with a machine-parseable input format.

[0099] After receiving the intermediate representation from the symbolic reasoning unit, the neural code generation unit uses a graph neural network encoder to extract features from the intermediate representation. The graph neural network captures the syntactic dependencies in the intermediate representation through node embedding and edge weight learning, encoding the tree-like or graph-like intermediate representation into a high-dimensional feature vector. During the encoding process, the graph neural network aggregates the feature information of adjacent nodes. For example, in the microservice interface generation scenario, the association between parameter nodes and protocol rule edges is encoded into a vector representation, providing rich semantic context for the decoding stage.

[0100] The Transformer-based decoder generates interface source code based on feature vectors. The Transformer decoder uses a self-attention mechanism to focus on key elements in the feature vectors, progressively outputting token sequences to construct the source code. During decoding, a multi-head attention layer calculates the association weights between the input vector and the generated code, and a feedforward network layer predicts the probability distribution of the next token, ultimately generating interface code that conforms to the target protocol specification, such as a controller class for a RESTful protocol or a proto definition for a gRPC service. The output of the neural code generation unit is directly passed to the verification module for closed-loop testing, forming a complete pipeline from logical reasoning to code implementation.

[0101] The logical connection between the symbolic reasoning unit and the neural code generation unit is reflected in the transmission and transformation of intermediate representations. Symbolic reasoning ensures the compliance of the interface logic, while neural code generation automates the construction of syntactic structures. The two are bridged by feature vectors, connecting symbolic and neural representations. In practical applications, such as the generation of IoT device status reporting interfaces, the symbolic reasoning unit formalizes the constraints of device IDs and status codes into logical rules. The neural code generation unit outputs Go language code including streaming responses. The verification module tests interface performance through a digital twin sandbox, and iterative system optimization improves the reliability of the generated code.

[0102] Specifically, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes a verification module comprising:

[0103] The test case generator receives a set of compliance rules from the interface generation module, generates a sequence of test cases, and outputs the sequence of test cases to the sandbox executor.

[0104] The digital twin sandbox receives interface source code and configuration files from the interface generation module and builds a virtual deployment instance.

[0105] The sandbox executor receives test case sequences from the test case generator, injects the test case sequences into the virtual deployment instance, and outputs monitoring data to the analyzer.

[0106] The analyzer receives monitoring data from the sandbox executor, compares the monitoring data with the protocol specifications, generates a verification report and an optimized interface file, and outputs the verification report for system optimization.

[0107] The verification module, within the intelligent model system that automatically generates standard protocol interfaces, is responsible for verifying and optimizing the interface code. It enhances the compliance and reliability of the generated interfaces through a closed-loop testing process. The test case generator, based on the compliance rule set output by the interface generation module, automatically constructs a sequence of test cases covering parameter boundaries, abnormal inputs, and protocol consistency scenarios. For example, in microservice interface testing, it generates test cases including invalid data types or missing required fields to verify the robustness of the interface. The generation logic employs a rule derivation algorithm to transform protocol constraints into specific test steps, ensuring the comprehensiveness of the sequence.

[0108] After receiving the interface source code and configuration files, the digital twin sandbox constructs a virtual deployment instance to simulate the target runtime environment. The instantiation process utilizes containerization technology to isolate resources and replicate the hardware and software configurations of a real deployment. For example, in IoT device interface testing, it mirrors the memory and network conditions of an embedded system, providing a foundation for secure test execution. The sandbox construction supports dynamic resource allocation to adapt to the deployment requirements of different protocol interfaces.

[0109] The sandbox executor injects test case sequences into a virtual deployment instance, driving interface execution and collecting monitoring data in real time. The injection mechanism achieves interaction between test cases and the instance through API hooks or traffic redirection. Monitoring data includes interface response latency, status code distribution, and error logs, thereby capturing runtime behavior. The execution process emphasizes non-intrusive data collection to avoid interfering with the normal operation of the instance.

[0110] The analyzer compares monitoring data with protocol specifications, identifies discrepancies, and generates a structured verification report. The comparison algorithm, based on a rule engine, matches actual output with expected protocol values; for example, it checks whether the HTTP methods of RESTful interfaces conform to specifications. The report outputs error type classifications and optimization instructions. The verification report is fed back to the self-learning module, driving the parsing logic and knowledge base updates, forming an iterative optimization loop. Each step is connected via data flow; test case generation guides sandbox execution, and analysis results provide feedback for correction instructions, constructing a complete quality assurance chain from code generation to verification. In practical terminal applications, this module performs sandbox testing on driver adaptation interfaces, verifies peripheral protocol compatibility, and supports the automatic generation and optimization of cross-platform interfaces.

[0111] Specifically, the intelligent model system for automatically generating standard protocol interfaces of the present invention further includes:

[0112] The symbolic reasoning unit of the interface generation module receives a set of compliance rules from the protocol knowledge base module and standardized requirement parameters from the requirement parsing module, and generates a first-generation intermediate representation based on the set of compliance rules and standardized requirement parameters.

[0113] The neural code generation unit receives the first-generation intermediate representation from the symbolic reasoning unit, generates the initial interface code based on the first-generation intermediate representation, and outputs the initial interface code to the verification module.

[0114] The verification module receives the initial interface code from the interface generation module, verifies the initial interface code, generates a correction instruction when the verification fails, and outputs the correction instruction to the symbolic reasoning unit of the interface generation module.

[0115] The symbolic reasoning unit receives correction instructions from the verification module, re-solves the constraints according to the correction instructions, and generates the second-generation intermediate representation.

[0116] The neural code generation unit receives the second-generation intermediate representation from the symbolic reasoning unit, generates updated interface code based on the second-generation intermediate representation, and outputs the updated interface code to the verification module. This process is repeated until the verification module outputs a verification pass signal.

[0117] The symbolic reasoning unit performs formal logic description and constraint solving based on the compliance rule set provided by the protocol knowledge base module and the standardized requirement parameters output by the requirement parsing module. The compliance rule set includes the syntax rules and data constraints for the target protocol type, while the standardized requirement parameters cover parameter names, data types, and boundary conditions. The symbolic reasoning unit uses first-order logic predicates to abstractly represent the input parameters, verifies constraint consistency through a theorem prover, and generates a structured first-generation intermediate representation. This intermediate representation acts as a bridge, transforming requirement semantics into machine-parseable intermediate code, laying the foundation for subsequent code generation.

[0118] After receiving the first-generation intermediate representation, the neural code generation unit extracts feature vectors using a graph neural network encoder, and then generates the initial interface source code using a Transformer-based decoder. The initial interface code includes interface definitions, data serialization logic, and error handling mechanisms, directly mapping to standardized requirement parameters and protocol rules. The neural code generation unit outputs the code to the verification module, completing the transformation from logical description to an executable implementation.

[0119] The verification module constructs a virtual deployment environment using a digital twin sandbox to perform runtime testing on the initial interface code. The test case generator constructs test cases for abnormal inputs, boundary values, and concurrent scenarios based on a compliance rule set. The sandbox executor injects test sequences and monitors the interface response. The analyzer compares the monitoring data with the protocol specification to identify code logic deviations or performance bottlenecks. When a verification failure is detected, the verification module generates correction instructions, clarifies the error type and optimization direction, and feeds them back to the symbolic reasoning unit.

[0120] The symbolic reasoning unit re-solves the constraints based on the correction instructions, adjusting the logical predicate descriptions or expanding the constraints. These correction instructions may involve data type mapping correction, protocol rule adaptation optimization, or supplementary exception handling logic. The symbolic reasoning unit generates a second-generation intermediate representation through iterative solving, enhancing the accuracy and completeness of the formal description.

[0121] The neural code generation unit updates the interface code based on the second-generation intermediate representation, optimizing the code structure or fixing known defects. The updated interface code is then submitted to the verification module for testing, forming a closed-loop iteration of generation-verification-correction. This process is repeated until the verification module outputs a verification pass signal, indicating that the interface code fully complies with the protocol specifications and requirement parameters. This iterative mechanism effectively improves code generation quality, reduces manual intervention, and enables automated pipelines for interface development in scenarios such as microservice architectures or IoT device management.

[0122] Specifically, the intelligent model system for automatically generating standard protocol interfaces of the present invention includes a self-learning module comprising:

[0123] The policy network receives verification reports from the verification module and satisfaction scores from user input. Based on the current interface, it generates task feature vectors, error type distributions in the verification report, and user satisfaction scores, and generates parameter update instructions, which are then output to the parameter update unit.

[0124] The parameter update unit receives parameter update instructions from the policy network and updates the neural network model parameters of the requirement parsing module and the neural code generation unit parameters of the interface generation module according to the parameter update instructions.

[0125] The self-learning module plays a crucial role in the intelligent model system that automatically generates standard protocol interfaces, dynamically optimizing system performance based on verification feedback. This module, through the collaborative work of the policy network and parameter update unit, achieves a closed-loop learning mechanism from verification results to model parameter adjustment. The policy network receives the verification report output by the verification module and the user's satisfaction score. The verification report includes the error type distribution and performance metrics during the interface generation task execution. The policy network performs correlation analysis between the feature vector of the current interface generation task and the error data in the verification report. The feature vector is extracted from the running status of the requirement parsing module and the interface generation module, such as parameter parsing accuracy or code generation consistency. Based on the error type distribution and user satisfaction score, the policy network uses a policy gradient algorithm to generate parameter update instructions. These instructions specify the direction and magnitude of parameter adjustment for the neural network model of the requirement parsing module and the neural code generation unit of the interface generation module.

[0126] After receiving parameter update instructions from the policy network, the parameter update unit executes the specific parameter update operations. Based on the adjustment direction in the parameter update instructions, the parameter update unit applies the stochastic gradient descent algorithm to optimize the neural network model parameters of the requirement parsing module, such as adjusting the attention weights of the natural language processing unit or the confidence calculation logic of the structured parsing unit. Simultaneously, the parameter update unit updates the Transformer architecture parameters of the neural code generation unit in the interface generation module, such as modifying the inter-layer connection weights of the decoder to improve code generation quality. During the parameter update process, the system records the parameter change history for easy retrospective analysis of learning effectiveness.

[0127] The logical relationship between the policy network and the parameter update unit is manifested as a loop of instruction transmission and execution feedback. The parameter update instructions generated by the policy network are based on multi-source data fusion analysis, and the execution results of the parameter update unit are fed back to the verification module through subsequent interface tasks, forming a new verification report input into the policy network. This data flow design enables the self-learning module to continuously adapt to protocol evolution and changes in user needs. In practical applications of microservice architecture, for example, when the system processes task generation by calling the user service interface of the order service, if the policy network detects a high parameter mapping error rate, it will generate parameter update instructions that match the data type. The parameter update unit will then adjust the model parameters accordingly, significantly reducing the error rate of subsequent similar tasks.

[0128] The self-learning module's underlying technical solutions also include a quantitative processing mechanism for error type distribution. The policy network categorizes errors in the verification report into types such as data type mismatch, protocol rule conflicts, or performance bottlenecks, and assigns weight coefficients to each type of error. User satisfaction scores are collected through the interactive interface and converted into numerical indicators to participate in instruction generation. When updating parameters, the parameter update unit considers the current state and historical change trends of the model parameters to avoid over-adjustment that could lead to system instability. Through iterative optimization, the self-learning module improves the system's adaptability in complex scenarios. For example, during the generation of the IoT device status reporting interface, the self-learning module gradually optimizes the adaptation logic of the streaming protocol based on verification feedback.

[0129] Specifically, the intelligent model system for automatically generating standard protocol interfaces of the present invention further includes:

[0130] The conflict case recording unit receives verification failure information from the analyzer of the verification module, records the combination of input parameters that caused the interface response to be abnormal, extracts key features, and outputs key features to the temporary node insertion unit.

[0131] The temporary node insertion unit receives key features from the conflict case record unit, inserts the key features as temporary nodes into the protocol knowledge graph, and creates relationship edges from the temporary nodes to the relevant protocol rule nodes.

[0132] The parameter feature checking unit receives extracted parameter features from the natural language processing unit of the requirement parsing module and key features from the temporary node insertion unit. It checks the similarity between the parameter features and the key features. When the similarity exceeds a preset threshold, it outputs a confirmation prompt to the interactive interface.

[0133] The conflict case recording unit continuously monitors the analyzer output of the verification module during system operation, automatically capturing verification failure information when interface verification fails. This unit structurally records the input parameter combinations that cause abnormal interface responses. For example, in a microservice architecture, if a parameter type mismatch exception occurs when the order service calls the user service, the system will completely save the parameter names, data types, and constraints in the abnormal request. Key features are extracted using feature engineering methods, including parameter sequence patterns, numerical distribution characteristics, and protocol constraint violation types, forming a standardized feature vector that is output to the temporary node insertion unit.

[0134] After receiving key feature vectors, the ephemeral node insertion unit dynamically updates the topology of the protocol knowledge graph. This unit maps feature vectors to ephemeral nodes in the graph and establishes semantic connections with existing protocol rule nodes. Specifically, the system employs an incremental update mechanism from the graph database, adding timestamps and confidence weights to the ephemeral nodes. For example, in IoT device driver adaptation scenarios, when a protocol conflict is detected due to a specific combination of hardware model and operating system version parameters, the system creates an ephemeral node pointing to the relevant driver protocol rule node, forming a knowledge representation of the abnormal case.

[0135] The parameter feature checking unit runs synchronously during the requirements parsing phase. It obtains real-time extracted parameter features from the natural language processing unit and accesses the key feature library maintained by the temporary node insertion unit. This unit uses a similarity calculation algorithm based on the vector space model to compare the feature vectors of the currently parsed parameters with those of historical conflict cases. When the similarity calculation result exceeds a preset threshold, a confirmation prompt mechanism is triggered on the interactive interface. For example, during the generation of cross-protocol interfaces in a government system, if the system detects that the current parameter features are highly similar to historical cases of abnormal switching between RESTful and gRPC protocols, it will send a parameter verification confirmation request to the user.

[0136] This invention establishes a closed-loop learning mechanism in its data processing chain. The conflict case recording unit provides abnormal sample collection, the temporary node insertion unit dynamically expands the knowledge graph, and the parameter feature checking unit performs proactive risk warnings. These three units work collaboratively through data flow. The output of the conflict case recording unit serves as the input to the temporary node insertion unit, while the updated knowledge graph provided by the temporary node insertion unit provides the query basis for the parameter feature checking unit. This design enables the system to accumulate practical experience, identify potential protocol adaptation risks in similar scenarios in advance, and improve the robustness of interface generation.

[0137] In practical terminal applications, when a user installs a Windows application through the application engine, the system captures peripheral driver parameter anomalies through the conflict case recording unit. The temporary node insertion unit marks incompatible hardware-system combinations in the protocol knowledge graph. Subsequently, when the user requests to install a similar application, the parameter feature checking unit will proactively alert the user to driver compatibility risks. This mechanism effectively reduces the rate of repeated errors in cross-platform interface generation, demonstrating the system's adaptability to complex application scenarios.

[0138] Specifically, the intelligent model system for automatically generating standard protocol interfaces of the present invention further includes:

[0139] The statistics unit receives protocol rule call records from the interface generation module, counts the call frequency and success rate of different protocol rules, generates a protocol attention weight vector, and outputs the protocol attention weight vector to the protocol evolution tracking unit.

[0140] The protocol evolution tracking unit receives the protocol attention weight vector from the statistics unit and adjusts the crawling frequency and update depth of the web crawler according to the protocol attention weight vector.

[0141] The protocol knowledge graph receives protocol attention weight vectors from the statistical unit and sorts the rule nodes according to the protocol attention weight vectors when responding to queries.

[0142] The statistics unit monitors the protocol rule call records generated by the interface in real time, collecting the call count and execution result data for each protocol rule. This unit uses a sliding window algorithm to maintain the recent call sequence, calculates the call frequency ratio and success rate of protocol rules, and generates a protocol attention weight vector including priority weights. For example, in a terminal driver adaptation scenario, the statistics unit records the call frequency of the RESTful protocol on the hardware detection interface and dynamically updates the weight values ​​based on the interface response success rate.

[0143] After receiving the protocol attention weight vector, the protocol evolution tracking unit parses the protocol rule priority information within the vector. Based on the high-priority protocol rule identifiers in the weight vector, the unit adjusts the web crawler's crawling strategy, increasing the access frequency to the publication addresses of high-frequency protocol standards and expanding the document parsing depth to capture detailed changes. For example, for gRPC streaming rules frequently used in government systems, the crawler focuses on the version update logs of the official protocol repository, comparing the differences in the abstract syntax tree to identify key change points.

[0144] After integrating protocol attention weight vectors into the protocol knowledge graph, a weight ranking mechanism is introduced when responding to query requests. The graph query engine dynamically sorts rule nodes based on the weight vectors, prioritizing high-weight nodes for matching user query parameters. For example, during the generation of the application installation interface for Four Symbols Guardian, the knowledge graph prioritizes recommending frequently called domestic software adaptation protocol nodes, improving interface generation efficiency.

[0145] The weight vectors generated by the statistical unit synchronously drive the collaborative optimization of the protocol evolution tracking unit and the protocol knowledge graph. The protocol evolution tracking unit ensures the timeliness of the protocol rule base by adjusting crawler behavior, while the protocol knowledge graph improves query accuracy through a sorting mechanism, forming a closed-loop adaptive process from data statistics to knowledge base optimization. In the cross-platform interface generation task, the system dynamically allocates resources through weight vectors, prioritizing the updating and retrieval performance of high-frequency protocol rules.

[0146] This invention proposes a complete automated solution to address the low development efficiency and extended project cycles caused by the reliance on manual interface writing in existing technologies. The intelligent model system for automatically generating standard protocol interfaces utilizes a modular design to achieve end-to-end processing from requirement input to compliant interface output, effectively resolving the issues of high repetition and manual dependence in interface development during cross-service collaboration in microservice architectures.

[0147] In the initial stage of the system, the requirement acquisition module obtains user input requirement information, which exists in the form of natural language descriptions or structured data. For example, in a development scenario, a user might describe "developing a user query interface that supports retrieving name and age by user ID, using the RESTful protocol." The requirement acquisition module then passes the requirement information to the requirement parsing module, which employs a dual-path collaborative processing mechanism to improve parsing accuracy. The natural language processing unit performs sequence labeling on unstructured text, extracting entity parameters such as parameter names and data types. The structured parsing unit processes JSON or tabular input in parallel, parsing field constraints. The parameter fusion unit integrates the two types of outputs using a confidence-weighted algorithm to generate standardized requirement parameters, including parameter names, data types, constraints, and target protocol types. This design avoids misunderstandings that can occur in manual requirement analysis. For example, in a scenario where the order service calls the user service, the fusion unit integrates text descriptions and template data, outputting unified parameters for subsequent modules to use.

[0148] The protocol knowledge base module receives the target protocol type from standardized requirement parameters and outputs a compliance rule set based on the protocol knowledge graph. The protocol knowledge graph stores the relationship edges between protocol syntax element entity nodes and protocol rules, such as the HTTP method node and status code rule edge for the RESTful protocol. The graph matching unit uses the extracted entity parameters as query vectors and performs multi-hop traversal in the knowledge graph to find semantically similar nodes. When the semantic similarity score is lower than a preset threshold, the correction unit activates the graph-guided attention mechanism to correct the parameter mapping relationship. The version sniffer periodically accesses the protocol standard release address, identifies updates through hash value comparison, the differential analyzer compares the abstract syntax trees of the old and new versions of the documents to locate change points, and the graph update unit incrementally updates the knowledge graph nodes to ensure the system adapts to protocol evolution.

[0149] The interface generation module generates interface source code, configuration files, and interface documentation based on standardized requirement parameters and compliance rule sets. The symbolic reasoning unit uses first-order logic predicates to formally describe data types and constraints, generating intermediate representations through a theorem prover. The neural code generation unit uses a graph neural network encoder to encode the intermediate representations into feature vectors, and a decoder based on the Transformer architecture generates the interface source code. For example, in an IoT device status reporting scenario, the symbolic reasoning unit formalizes the mapping constraints between device IDs and status codes into logical rules, and the neural code generation unit outputs gRPC interface code including streaming responses. The verification module constructs virtual deployment instances through a digital twin sandbox. The test case generator constructs test sequences based on the compliance rule set, the sandbox executor injects test cases and monitors interface responses, and the analyzer compares the monitoring data with the protocol specifications to generate a verification report. When an anomaly is detected, the verification module generates correction instructions to drive iterative optimization of the interface generation module until a verification pass signal is output.

[0150] The self-learning module updates system parameters based on the verification report. The strategy network integration interface generates task feature vectors, error type distributions, and user satisfaction scores to generate parameter update instructions. The parameter update unit adjusts the neural network model parameters of the requirement parsing module and the parameters of the neural code generation unit. The conflict case recording unit captures verification failure information, extracts key features as temporary nodes and inserts them into the protocol knowledge graph. The parameter feature checking unit compares similarities in subsequent parsing and triggers interactive confirmation prompts. The statistics unit analyzes the frequency and success rate of protocol rule calls and generates attention weight vectors. The protocol evolution tracking unit adjusts the web crawler crawling strategy, and the protocol knowledge graph optimizes query ranking.

[0151] The system forms a closed-loop process of requirements analysis, protocol adaptation, code generation, and verification optimization. Automation reduces human error rates and improves the consistency and reliability of interface development. Each module collaborates closely through data flow; for example, requirements analysis results drive protocol knowledge retrieval, verification feedback optimizes code generation strategies, and self-learning data updates the parsing logic, ultimately achieving efficient interface generation in complex application scenarios.

[0152] This invention, an intelligent model system for automatically generating standard protocol interfaces, effectively solves the problems of low efficiency and extended cycle times associated with manual interface writing through modular automated design. Existing manual processes involve multiple stages, including requirements communication, protocol learning, code writing, and testing and verification. This not only introduces communication costs but also leads to increased error rates and project delays due to differences in individual skills. This invention transforms discrete manual activities into a coherent machine execution pipeline, forming an end-to-end closed loop from requirement input to compliant interface output.

[0153] The system initially receives user-input requirements from the requirements gathering module. These requirements are presented in natural language or structured data formats; for example, a developer in a microservices scenario might submit a request to "create a user query interface that supports retrieving names by ID, using a RESTful protocol." The requirements parsing module uses a natural language processing unit to parse the text and extract entity parameters, while a structured parsing unit processes table or JSON format configurations. The parameter fusion unit uses a confidence-weighted algorithm to integrate the dual outputs, generating standardized requirements parameters that include parameter names, data types, and protocol types. This automated parsing mechanism avoids misunderstandings inherent in manual requirements analysis. In instances where the order service calls the user service, the system accurately maps parameter constraints, reducing subsequent rework.

[0154] The protocol knowledge base module stores protocol rule entity nodes and relationship edges based on a knowledge graph. The graph matching unit uses standardized parameters as query vectors for multi-hop traversal to quickly locate semantically similar protocol rule nodes. A version sniffer periodically detects protocol standard updates, a differential analyzer identifies change points through abstract syntax tree comparison, and the graph update unit incrementally updates the knowledge base. When handling cross-departmental data interaction in government systems, the system dynamically adapts to RESTful and gRPC protocol rules, significantly reducing the protocol learning cost for developers.

[0155] The symbolic reasoning unit of the interface generation module uses first-order logic predicates to formally describe parameter constraints, while the neural code generation unit, based on the Transformer architecture, transforms intermediate representations into executable source code. In IoT device monitoring scenarios, the system automatically generates gRPC interface code, including exception handling mechanisms, ensuring code standardization and consistency, and replacing the manual writing of error-prone processes.

[0156] The verification module constructs a virtual deployment environment through a digital twin sandbox, injecting boundary value tests and concurrent scenario test cases. The analyzer compares the actual response with the protocol specification to generate optimization instructions. The self-learning module collects the error type distribution from the verification report, and the policy network dynamically adjusts the parsing model parameters, forming an iterative optimization loop. When an anomaly is detected in the driver adaptation interface parameter mapping, the system automatically corrects the knowledge graph node relationships, improving the accuracy of subsequent generation.

[0157] This invention's system seamlessly connects various modules through data flow, drives protocol matching based on requirement analysis results, optimizes code generation strategies through verification feedback, and continuously improves system adaptability through a self-learning mechanism. In actual terminal deployment, this automated pipeline reduces the microservice interface development cycle from several days of manual work to minutes by machine, while standardization reduces the risk of human error, achieving a dual improvement in efficiency and quality.

[0158] The intelligent model system that automatically generates standard protocol interfaces addresses the problems of low development efficiency and extended project cycles caused by the reliance on manual interface writing in existing technologies. It achieves end-to-end processing through modular automated design.

[0159] Embodiment 1 of this invention: In a microservice architecture, the system generates requirements for the interface of the order service calling the user service. The user inputs a natural language description: "Develop a user query interface that supports obtaining name and age by user ID, using the RESTful protocol." The requirement acquisition module obtains this requirement information and transmits it to the requirement parsing module. The natural language processing unit performs sequence annotation on the unstructured text, extracting entity parameters such as parameter name user_id and data type integer. The structured parsing unit parses the field constraints in the JSON configuration template uploaded by the user in parallel. The parameter fusion unit generates standardized requirement parameters, including the target protocol type RESTful, through confidence-weighted fusion. The protocol knowledge base module uses protocol knowledge graph matching rules. The graph matching unit performs multi-hop traversal using entity parameters as query vectors to locate HTTP method nodes and status code rule edges. The version sniffer periodically detects protocol updates to ensure rule timeliness. The symbolic reasoning unit of the interface generation module uses first-order logic predicates to formally describe parameter constraints. The neural code generation unit outputs RESTful controller code based on the Transformer architecture. The verification module verifies the interface response by injecting test cases into a digital twin sandbox. The self-learning module adjusts model parameters based on the verification report. This invention compresses development tasks that take several days to complete into minutes, reducing communication costs and error rates.

[0160] Embodiment 2 of this invention: In an IoT device management scenario, the system processes device status reporting interface generation requirements. Users submit structured data specifying device ID, status code, and timestamp parameters, with the target protocol type being gRPC. The requirement parsing module's structured parsing unit parses the tabular input, extracts field types and validation rules, and the parameter fusion unit integrates them into standardized requirement parameters. The protocol knowledge base module's graph matching unit matches streaming transmission rule nodes in the knowledge graph, the differential analyzer detects gRPC protocol version changes, and the graph update unit incrementally updates the syntax rules. The interface generation module's symbolic reasoning unit formalizes parameter mapping constraints into logical rules, and the neural code generation unit outputs a proto file containing the streaming response and Go language code. The verification module verifies interface robustness through boundary value testing, and the self-learning module optimizes the neural code generation unit parameters based on error distribution. This system achieves automatic cross-platform interface adaptation, improving the deployment efficiency of the IoT device monitoring system.

[0161] The innovative aspects of this invention are as follows:

[0162] The innovation lies in replacing existing discrete manual processes with end-to-end closed-loop processing. The system employs dual-path fusion parsing technology. The natural language processing unit of the requirement parsing module extracts entity parameters from the unstructured text input by the user through sequence labeling, while the structured parsing unit parses configuration data such as JSON or tables. The parameter fusion unit integrates and outputs standardized requirement parameters through a confidence-weighted algorithm, avoiding misunderstandings of requirements caused by communication discrepancies when the order service calls the user service in a microservice architecture. The protocol knowledge base module introduces a dynamic knowledge graph management mechanism. The protocol knowledge graph stores entity nodes of protocol syntax elements and rule relationship edges. The graph matching unit performs multi-hop traversal to achieve semantic matching between parameters and rules. The version sniffer and differential analyzer periodically detect protocol updates, ensuring that the system dynamically adapts to RESTful and gRPC protocol rules in cross-departmental data interaction scenarios in government systems.

[0163] The interface generation module combines symbolic reasoning and neural code generation technologies. The symbolic reasoning unit uses first-order logic predicates to formally describe data type constraints, and the theorem prover solves to generate intermediate representations. The neural code generation unit outputs executable code based on the Transformer architecture, such as automatically generating gRPC interface source code including streaming responses in IoT device status reporting requirements. The verification module constructs virtual deployment instances through a digital twin sandbox, injects boundary value test sequences through a test case generator, and achieves closed-loop verification by comparing with protocol specifications through an analyzer. The self-learning module adjusts model parameters based on the error distribution in the verification report, and the policy network integrates user satisfaction scores to optimize the parsing logic, forming a continuously iterative intelligent optimization loop. This invention system shortens the development task from several days manually, significantly reducing the risk of project delays while ensuring code compliance.

Claims

1. An intelligent model system for automatically generating standard protocol interfaces, characterized in that, include: The requirement acquisition module obtains requirement information from user input and outputs the requirement information to the requirement parsing module. The requirement parsing module receives requirement information from the requirement acquisition module, converts the requirement information into standardized requirement parameters including parameter names, data types, constraints, and target protocol types, and outputs the standardized requirement parameters to the protocol knowledge base module and the interface generation module. The protocol knowledge base module receives the target protocol type from the standardized requirement parameters from the requirement parsing module, and outputs the corresponding compliance rule set to the interface generation module based on the target protocol type. The interface generation module receives standardized requirement parameters from the requirement parsing module and a compliance rule set from the protocol knowledge base module. Based on the standardized requirement parameters and the compliance rule set, it generates interface source code, configuration files, and interface documentation, and outputs the interface source code and configuration files to the verification module. The verification module receives the interface source code and configuration file from the interface generation module, verifies and optimizes the interface source code and configuration file, generates a verification report and optimized interface source code and configuration file, and outputs the verification report for system optimization. The self-learning module receives verification reports from the verification module and updates the parsing logic of the requirement parsing module and the rule content stored in the protocol knowledge base module based on the verification reports. The requirements analysis module includes: The natural language processing unit receives unstructured text from the demand information from the demand acquisition module, performs sequence annotation on the unstructured text, extracts entity parameters, and outputs the entity parameters to the parameter fusion unit. The structured parsing unit receives structured data from the requirement information from the requirement acquisition module, parses the structured data, and outputs the parsing results to the parameter fusion unit. The parameter fusion unit receives entity parameters from the natural language processing unit and parsing results from the structured parsing unit. It performs confidence-weighted fusion of the entity parameters and parsing results, generates and outputs standardized requirement parameters to the protocol knowledge base module and the interface generation module. The protocol knowledge base module includes: Protocol knowledge graph, which stores the relationship edges between protocol syntax element entity nodes and protocol rules; The graph matching unit receives the extracted entity parameters from the natural language processing unit of the requirement parsing module, uses the entity parameters as query vectors to perform multi-hop traversal in the protocol knowledge graph, searches for semantically similar entity nodes, and outputs the matching results to the correction unit. The correction unit receives the matching results from the graph matching unit, calculates the semantic similarity score between the entity parameters and the graph nodes, and when the score is lower than a preset threshold, activates the graph-guided attention mechanism to correct the entity parameters and outputs the corrected entity parameters to the natural language processing unit.

2. The intelligent model system for automatically generating standard protocol interfaces according to claim 1, characterized in that, The protocol knowledge base module includes: The version sniffer periodically accesses a predefined protocol standard publication address, identifies potential updates by comparing the hash values ​​of consecutive version documents, and outputs update signals to the differential analyzer. The differential analyzer receives update signals from the version sniffer, compares the abstract syntax trees of the old and new versions of the document, locates the changes in syntax rules, and outputs the changes to the graph update unit. The graph update unit receives change points from the differential analyzer, updates the protocol knowledge graph with the change points as new nodes or relation edges, and adds time decay weights to the new and changed graph elements.

3. The intelligent model system for automatically generating standard protocol interfaces according to claim 2, characterized in that, The interface generation module includes: The symbolic reasoning unit receives a set of compliance rules from the protocol knowledge base module and standardized requirement parameters from the requirement parsing module. It uses first-order logic predicates to formally describe data types and constraints, solves the problem through the theorem prover to generate intermediate representations, and outputs the intermediate representations to the neural code generation unit. The neural code generation unit receives intermediate representations from the symbolic reasoning unit, uses a graph neural network structure to encode the intermediate representations into feature vectors, and a decoder based on the Transformer architecture generates interface source code based on the feature vectors and outputs the interface source code to the verification module.

4. The intelligent model system for automatically generating standard protocol interfaces according to claim 3, characterized in that, The verification module includes: The test case generator receives a set of compliance rules from the interface generation module, generates a sequence of test cases, and outputs the sequence of test cases to the sandbox executor. The digital twin sandbox receives interface source code and configuration files from the interface generation module and builds a virtual deployment instance. The sandbox executor receives test case sequences from the test case generator, injects the test case sequences into the virtual deployment instance, and outputs monitoring data to the analyzer. The analyzer receives monitoring data from the sandbox executor, compares the monitoring data with the protocol specifications, generates a verification report and optimized interface source code and configuration files, and outputs the verification report for system optimization.

5. The intelligent model system for automatically generating standard protocol interfaces according to claim 4, characterized in that, Also includes: The symbolic reasoning unit of the interface generation module receives a set of compliance rules from the protocol knowledge base module and standardized requirement parameters from the requirement parsing module, and generates a first-generation intermediate representation based on the set of compliance rules and standardized requirement parameters. The neural code generation unit receives the first-generation intermediate representation from the symbolic reasoning unit, generates the initial interface code based on the first-generation intermediate representation, and outputs the initial interface code to the verification module. The verification module receives the initial interface code from the interface generation module, verifies the initial interface code, generates a correction instruction when the verification fails, and outputs the correction instruction to the symbolic reasoning unit of the interface generation module. The symbolic reasoning unit receives correction instructions from the verification module, re-solves the constraints according to the correction instructions, and generates the second-generation intermediate representation. The neural code generation unit receives the second-generation intermediate representation from the symbolic reasoning unit, generates updated interface code based on the second-generation intermediate representation, and outputs the updated interface code to the verification module. This process is repeated until the verification module outputs a verification pass signal.

6. The intelligent model system for automatically generating standard protocol interfaces according to claim 5, characterized in that, The self-learning module includes: The policy network receives verification reports from the verification module and satisfaction scores from user input. Based on the current interface, it generates task feature vectors, error type distributions in the verification report, and user satisfaction scores, and generates parameter update instructions, which are then output to the parameter update unit. The parameter update unit receives parameter update instructions from the policy network and updates the neural network model parameters of the requirement parsing module and the neural code generation unit parameters of the interface generation module according to the parameter update instructions.

7. The intelligent model system for automatically generating standard protocol interfaces according to claim 6, characterized in that, Also includes: The conflict case recording unit receives verification failure information from the analyzer of the verification module, records the combination of input parameters that caused the interface response to be abnormal, extracts key features, and outputs key features to the temporary node insertion unit. The temporary node insertion unit receives key features from the conflict case record unit, inserts the key features as temporary nodes into the protocol knowledge graph, and creates relationship edges from the temporary nodes to the relevant protocol rule nodes. The parameter feature checking unit receives extracted parameter features from the natural language processing unit of the requirement parsing module and key features from the temporary node insertion unit. It checks the similarity between the parameter features and the key features. When the similarity exceeds a preset threshold, it outputs a confirmation prompt to the interactive interface.

8. The intelligent model system for automatically generating standard protocol interfaces according to claim 7, characterized in that, Also includes: The statistics unit receives protocol rule call records from the interface generation module, counts the call frequency and success rate of different protocol rules, generates a protocol attention weight vector, and outputs the protocol attention weight vector to the protocol evolution tracking unit. The protocol evolution tracking unit receives the protocol attention weight vector from the statistics unit and adjusts the crawling frequency and update depth of the web crawler according to the protocol attention weight vector. The protocol knowledge graph receives protocol attention weight vectors from the statistical unit and sorts the rule nodes according to the protocol attention weight vectors when responding to queries.

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